day_26_dense_community_detection_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7423be59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for the explode statement; convert string to list\n",
    "from ast import literal_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "102626ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for visualization\n",
    "from IPython.display import SVG \n",
    "from sknetwork.visualization import svg_graph \n",
    "from sknetwork.data import Bunch \n",
    "from sknetwork.ranking import PageRank \n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5):\n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_array(G, nodelist=None, dtype=None, weight='weight', format='csr')\n",
    "    adjacency = csr_matrix(adjacency) # fix to weird sknetwork-csr issue; comment out to troubleshoot\n",
    "    \n",
    "    names = np.array(list(G.nodes))\n",
    "    \n",
    "    graph = Bunch()\n",
    "    graph.adjacency = adjacency\n",
    "    graph.names = np.array(names)\n",
    "    \n",
    "    pagerank = PageRank()\n",
    "    scores = pagerank.fit_transform(adjacency)\n",
    "\n",
    "    if show_names:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width)\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width)\n",
    "    \n",
    "    return SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a12aec",
   "metadata": {},
   "source": [
    "# Create Graph using Bipartite Projection on Arxiv Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "443c3cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authors</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dustin Nguyen</td>\n",
       "      <td>Neural ODEs as a discovery tool to characteriz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yuan-Sen Ting</td>\n",
       "      <td>Neural ODEs as a discovery tool to characteriz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Todd A. Thompson</td>\n",
       "      <td>Neural ODEs as a discovery tool to characteriz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sebastian Lopez</td>\n",
       "      <td>Neural ODEs as a discovery tool to characteriz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Laura A. Lopez</td>\n",
       "      <td>Neural ODEs as a discovery tool to characteriz...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            authors                                              title\n",
       "0     Dustin Nguyen  Neural ODEs as a discovery tool to characteriz...\n",
       "1     Yuan-Sen Ting  Neural ODEs as a discovery tool to characteriz...\n",
       "2  Todd A. Thompson  Neural ODEs as a discovery tool to characteriz...\n",
       "3   Sebastian Lopez  Neural ODEs as a discovery tool to characteriz...\n",
       "4    Laura A. Lopez  Neural ODEs as a discovery tool to characteriz..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 'data/arxiv_network_science_data.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "df['authors'] = df['authors'].apply(literal_eval)\n",
    "\n",
    "df = df[['authors', 'title']]\n",
    "df = df.explode('authors')\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# fix the & so that scikit-network stops crashing\n",
    "df['authors'] = df['authors'].str.replace('&', 'and')\n",
    "df['title'] = df['title'].str.replace('&', 'and')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5726918",
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms import bipartite\n",
    "\n",
    "B = nx.from_pandas_edgelist(df, source='authors', target='title')\n",
    "G = bipartite.projected_graph(B, df['authors'])\n",
    "\n",
    "G = nx.k_core(G, 2) # no isolates or two-node groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbee3229",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'networkx' has no attribute 'info'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(nx\u001b[38;5;241m.\u001b[39minfo(G))\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'networkx' has no attribute 'info'"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9952d45",
   "metadata": {},
   "source": [
    "# Community Detection + Community Analysis\n",
    "\n",
    "There's two parts to what I am attempting today.\n",
    "- I need to use community detection to identify communities in the network.\n",
    "- I can then use network science and data science to identify interesting communities\n",
    "\n",
    "By interesting, I mean not stars. I am looking for complex ecosystems. So, I will identify communities, then attempt to detect more complex ecosystems by using triangles and density."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d07b3a",
   "metadata": {},
   "source": [
    "## Community Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46f88f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import community as community_louvain\n",
    "\n",
    "partition = community_louvain.best_partition(G, resolution=0.01, random_state=1337) # 10 was good for this network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b81c14c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "community\n",
       "10829    186\n",
       "6171     167\n",
       "6852     145\n",
       "3482     138\n",
       "6082     128\n",
       "10435    118\n",
       "7308     110\n",
       "6251     104\n",
       "4165     102\n",
       "3330     100\n",
       "3593      91\n",
       "3275      83\n",
       "6732      71\n",
       "9863      69\n",
       "7890      68\n",
       "6211      64\n",
       "6568      64\n",
       "2590      63\n",
       "11093     59\n",
       "7645      58\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_df = pd.DataFrame().from_dict(partition, orient='index')\n",
    "community_df.reset_index(inplace=True)\n",
    "community_df.columns = ['node', 'community']\n",
    "\n",
    "# remove junk; identify relevant communities\n",
    "\n",
    "community_df['community'].value_counts()[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ed70761",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10829</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6171</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6852</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3482</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6082</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   community  node_count\n",
       "0      10829         186\n",
       "1       6171         167\n",
       "2       6852         145\n",
       "3       3482         138\n",
       "4       6082         128"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_node_counts = community_df['community'].value_counts()\n",
    "\n",
    "community_size_df = pd.DataFrame(community_node_counts).reset_index()\n",
    "community_size_df.columns = ['community', 'node_count']\n",
    "\n",
    "# just to speed up this experiment\n",
    "community_size_df = community_size_df[community_size_df['node_count'] > 10]\n",
    "\n",
    "community_size_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77719ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(743, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_size_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a4daaa",
   "metadata": {},
   "source": [
    "## Capture Community Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88142c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dustin Nguyen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yuan-Sen Ting</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Todd A. Thompson</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sebastian Lopez</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Laura A. Lopez</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               node  community\n",
       "0     Dustin Nguyen          2\n",
       "1     Yuan-Sen Ting        397\n",
       "2  Todd A. Thompson          2\n",
       "3   Sebastian Lopez          2\n",
       "4    Laura A. Lopez          2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "024410cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10829</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   community  node_count\n",
       "0      10829         186"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_size_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e3ebd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_community_nodes(community_df, community):\n",
    "\n",
    "    return sorted(community_df[community_df['community']==community]['node'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8edef19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_community_context(community_size_df, community_df, G):\n",
    "\n",
    "    G = G.copy()\n",
    "\n",
    "    communities = {}\n",
    "\n",
    "    for row in community_size_df.iterrows():\n",
    "\n",
    "        data = row[1]\n",
    "        community = data['community']\n",
    "        node_count = data['node_count']\n",
    "        \n",
    "        nodes = get_community_nodes(community_df, community)\n",
    "        G_sub = G.subgraph(nodes)\n",
    "\n",
    "        # starting with density today; will add on more context later\n",
    "        density = nx.density(G_sub)\n",
    "        \n",
    "        communities[community] = {}\n",
    "\n",
    "        communities[community]['G'] = G_sub\n",
    "        communities[community]['density'] = density\n",
    "        communities[community]['node_count'] = len(G_sub.nodes)\n",
    "        \n",
    "    return communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82447ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "community_context_dict = get_community_context(community_size_df, community_df, G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb8dc673",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>G</th>\n",
       "      <th>density</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10829</td>\n",
       "      <td>(Nattida Samanso, Dong-Kyu Jung, Motoki Kino, ...</td>\n",
       "      <td>0.343156</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6171</td>\n",
       "      <td>(D. Cichon, K. Ni, L. Baudis, J. R. Angevaare,...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6852</td>\n",
       "      <td>(U. Straumann, D. Pitzl, P. Robmann, D. Wegene...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3482</td>\n",
       "      <td>(J. Peloton, J. -P. Rivet, M. Sabil, M. Masek,...</td>\n",
       "      <td>0.879509</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6082</td>\n",
       "      <td>(Raffaella Schneider, Tanja Hinderer, B. S. Sa...</td>\n",
       "      <td>0.61159</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   community                                                  G   density  \\\n",
       "0      10829  (Nattida Samanso, Dong-Kyu Jung, Motoki Kino, ...  0.343156   \n",
       "1       6171  (D. Cichon, K. Ni, L. Baudis, J. R. Angevaare,...       1.0   \n",
       "2       6852  (U. Straumann, D. Pitzl, P. Robmann, D. Wegene...       1.0   \n",
       "3       3482  (J. Peloton, J. -P. Rivet, M. Sabil, M. Masek,...  0.879509   \n",
       "4       6082  (Raffaella Schneider, Tanja Hinderer, B. S. Sa...   0.61159   \n",
       "\n",
       "  node_count  \n",
       "0        186  \n",
       "1        167  \n",
       "2        145  \n",
       "3        138  \n",
       "4        128  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_context_df = pd.DataFrame(community_context_dict).T.reset_index()\n",
    "community_context_df.columns = ['community', 'G', 'density', 'node_count']\n",
    "\n",
    "community_context_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "386bcb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sHHbYYfnWt76VxsbGJNnmivmaNWuqV98bGxuzadOmrF27dodrtqeurq76jfXvbAAAAFCaHo/2d6tUKmlvb8+IESPS2NiYhQsXVp/btGlTFi1alLFjxyZJRo8end69e3dY09zcnGeffba6BgAAAPZUtT35w6+66qpMmjQpw4YNy7p16zJ//vw88cQTeeSRR1JTU5MpU6Zk5syZGTlyZEaOHJmZM2dmn332yTnnnJMkqa+vz+TJkzNt2rQMGjQoAwcOzPTp03PIIYdk/PjxPXloAAAAsMt6NNp//etf5wtf+EKam5tTX1+fQw89NI888kgmTJiQJLn88suzcePGXHTRRVm7dm2OOOKIPProo+nfv3/1PW666abU1tbmzDPPzMaNGzNu3LjMmzcvvXr16qnDAgAAgC5R3O9p7wl+TzsAAEAZ/J72joq7px0AAAD4HdEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACF6tFonzVrVv78z/88/fv3z5AhQ3Lqqafm+eef77Dm/PPPT01NTYftk5/8ZIc17e3tufTSSzN48OD069cvp5xySlavXr07DwUAAAC6XI9G+6JFi3LxxRfnySefzMKFC/P2229n4sSJ2bBhQ4d1n/3sZ9Pc3FzdHn744Q7PT5kyJQsWLMj8+fOzePHirF+/PieddFK2bNmyOw8HAAAAulRtT/7wRx55pMPj2267LUOGDMny5cvzmc98prq/rq4ujY2N232P1tbW3Hrrrbnzzjszfvz4JMldd92VYcOG5bHHHsvxxx/ffQcAAAAA3aioe9pbW1uTJAMHDuyw/4knnsiQIUNy4IEH5itf+UrWrFlTfW758uXZvHlzJk6cWN3X1NSUUaNGZcmSJdv9Oe3t7Wlra+uwAQAAQGmKifZKpZKpU6fm05/+dEaNGlXdP2nSpNx99915/PHHc+ONN2bp0qU57rjj0t7eniRpaWlJnz59su+++3Z4v4aGhrS0tGz3Z82aNSv19fXVbdiwYd13YAAAANBJPfrx+P/skksuydNPP53Fixd32H/WWWdV//OoUaMyZsyYDB8+PA899FBOP/30Hb5fpVJJTU3Ndp+78sorM3Xq1OrjtrY24Q4AAEBxirjSfumll+bBBx/Mj3/84+y///6/d+3QoUMzfPjwvPDCC0mSxsbGbNq0KWvXru2wbs2aNWloaNjue9TV1WXAgAEdNgAAAChNj0Z7pVLJJZdckgceeCCPP/54RowY8Qdf88Ybb2TVqlUZOnRokmT06NHp3bt3Fi5cWF3T3NycZ599NmPHju222QEAAKC79ejH4y+++OLcc889+cd//Mf079+/eg96fX19+vbtm/Xr12fGjBk544wzMnTo0Lz88su56qqrMnjw4Jx22mnVtZMnT860adMyaNCgDBw4MNOnT88hhxxS/TZ5AAAA2BP1aLTPnTs3SXLMMcd02H/bbbfl/PPPT69evfLMM8/kjjvuyJtvvpmhQ4fm2GOPzX333Zf+/ftX1990002pra3NmWeemY0bN2bcuHGZN29eevXqtTsPBwAAALpUTaVSqfT0ED2tra0t9fX1aW1tdX87AABAD/roFQ/t8nu8fO2JXTBJ93qvHVrEF9EBAAAA2xLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQqE5F+8qVK7t6DgAAAOBdOhXtBxxwQI499tjcddddeeutt7p6JgAAACCdjPaf//zn+bM/+7NMmzYtjY2NueCCC/L//t//6+rZAAAAYK/WqWgfNWpUZs+enV/96le57bbb0tLSkk9/+tM5+OCDM3v27Pzbv/1bV88JAAAAe51d+iK62tranHbaafn7v//7XHfddXnxxRczffr07L///jn33HPT3NzcVXMCAADAXmeXon3ZsmW56KKLMnTo0MyePTvTp0/Piy++mMcffzy/+tWv8rnPfa6r5gQAAIC9Tm1nXjR79uzcdtttef7553PCCSfkjjvuyAknnJAPfOB3fwcwYsSIfPe7382f/umfdumwAAAAsDfpVLTPnTs3X/rSl/LFL34xjY2N213zkY98JLfeeusuDQcAAAB7s05F+wsvvPAH1/Tp0yfnnXdeZ94eAAAASCfvab/tttvyve99b5v93/ve93L77bfv8lAAAABAJ6P92muvzeDBg7fZP2TIkMycOXOXhwIAAAA6Ge2vvPJKRowYsc3+4cOH59VXX93loQAAAIBORvuQIUPy9NNPb7P/5z//eQYNGrTLQwEAAACdjPazzz47X/3qV/PjH/84W7ZsyZYtW/L444/nsssuy9lnn93VMwIAAMBeqVPfHn/NNdfklVdeybhx41Jb+7u32Lp1a84991z3tAMAAEAX6VS09+nTJ/fdd1/+6q/+Kj//+c/Tt2/fHHLIIRk+fHhXzwcAAAB7rU5F+zsOPPDAHHjggV01CwAAAPCfdCrat2zZknnz5uVHP/pR1qxZk61bt3Z4/vHHH++S4QAAAGBv1qlov+yyyzJv3ryceOKJGTVqVGpqarp6LgAAANjrdSra58+fn7//+7/PCSec0NXzAAAAAP+uU7/yrU+fPjnggAO6ehYAAADgP+lUtE+bNi3f+ta3UqlUunoeAAAA4N916uPxixcvzo9//OP84Ac/yMEHH5zevXt3eP6BBx7okuEAAABgb9apaP/whz+c0047ratnAQAAAP6TTkX7bbfd1tVzAAAAAO/SqXvak+Ttt9/OY489lu9+97tZt25dkuS1117L+vXru2w4AAAA2Jt16kr7K6+8ks9+9rN59dVX097engkTJqR///65/vrr89Zbb+U73/lOV88JAAAAe51OXWm/7LLLMmbMmKxduzZ9+/at7j/ttNPyox/9qMuGAwAAgL1Zp789/qc//Wn69OnTYf/w4cPzq1/9qksGAwAAgL1dp660b926NVu2bNlm/+rVq9O/f/9dHgoAAADoZLRPmDAhN998c/VxTU1N1q9fn2984xs54YQTumo2AAAA2Kt16uPxN910U4499tgcdNBBeeutt3LOOefkhRdeyODBg3Pvvfd29YwAAACwV+pUtDc1NWXFihW5995787Of/Sxbt27N5MmT8/nPf77DF9MBAAAAndepaE+Svn375ktf+lK+9KUvdeU8AAAAwL/rVLTfcccdv/f5c889t1PDAAAAAP+hU9F+2WWXdXi8efPm/Pa3v02fPn2yzz77iHYAAADoAp369vi1a9d22NavX5/nn38+n/70p30RHQAAAHSRTkX79owcOTLXXnvtNlfhAQAAgM7psmhPkl69euW1117ryrcEAACAvVan7ml/8MEHOzyuVCppbm7OnDlz8qlPfapLBgMAAIC9Xaei/dRTT+3wuKamJvvtt1+OO+643HjjjV0xFwAAAOz1OhXtW7du7eo5AAAAgHfp0nvaAQAAgK7TqSvtU6dOfc9rZ8+e3ZkfAQAAAHu9TkX7U089lZ/97Gd5++2387GPfSxJ8stf/jK9evXK4YcfXl1XU1PTNVMCAADAXqhT0X7yySenf//+uf3227PvvvsmSdauXZsvfvGLOeqoozJt2rQuHRIAAAD2Rp26p/3GG2/MrFmzqsGeJPvuu2+uueYa3x4PAAAAXaRT0d7W1pZf//rX2+xfs2ZN1q1bt8tDAQAAAJ2M9tNOOy1f/OIXc//992f16tVZvXp17r///kyePDmnn356V88IAAAAe6VO3dP+ne98J9OnT89f/MVfZPPmzb97o9raTJ48OTfccEOXDggAAAB7q05F+z777JNbbrklN9xwQ1588cVUKpUccMAB6devX1fPBwAAAHutTn08/h3Nzc1pbm7OgQcemH79+qVSqXTVXAAAALDX61S0v/HGGxk3blwOPPDAnHDCCWlubk6SfPnLX/br3gAAAKCLdCrav/a1r6V379559dVXs88++1T3n3XWWXnkkUe6bDgAAADYm3Uq2h999NFcd9112X///TvsHzlyZF555ZX3/D6zZs3Kn//5n6d///4ZMmRITj311Dz//PMd1lQqlcyYMSNNTU3p27dvjjnmmDz33HMd1rS3t+fSSy/N4MGD069fv5xyyilZvXp1Zw4NAAAAitGpaN+wYUOHK+zveP3111NXV/ee32fRokW5+OKL8+STT2bhwoV5++23M3HixGzYsKG65vrrr8/s2bMzZ86cLF26NI2NjZkwYUKH3wc/ZcqULFiwIPPnz8/ixYuzfv36nHTSSdmyZUtnDg8AAACKUFPpxLfHnXjiiTn88MPzV3/1V+nfv3+efvrpDB8+PGeffXa2bt2a+++/v1PD/Nu//VuGDBmSRYsW5TOf+UwqlUqampoyZcqUfP3rX0/yu6vqDQ0Nue6663LBBRektbU1++23X+68886cddZZSZLXXnstw4YNy8MPP5zjjz/+D/7ctra21NfXp7W1NQMGDOjU7AAAAOy6j17x0C6/x8vXntgFk3Sv99qhnfqVbzfccEOOOeaYLFu2LJs2bcrll1+e5557Lr/5zW/y05/+tNNDt7a2JkkGDhyYJFm5cmVaWloyceLE6pq6urocffTRWbJkSS644IIsX748mzdv7rCmqakpo0aNypIlS7Yb7e3t7Wlvb68+bmtr6/TMAAAA0F069fH4gw46KE8//XQ+8YlPZMKECdmwYUNOP/30PPXUU/mTP/mTTg1SqVQyderUfPrTn86oUaOSJC0tLUmShoaGDmsbGhqqz7W0tKRPnz7Zd999d7jm3WbNmpX6+vrqNmzYsE7NDAAAAN1pp6+0v3NV+7vf/W6++c1vdtkgl1xySZ5++uksXrx4m+dqamo6PK5UKtvse7fft+bKK6/M1KlTq4/b2tqEOwAAAMXZ6SvtvXv3zrPPPvsHo3lnXHrppXnwwQfz4x//uMM30jc2NibJNlfM16xZU7363tjYmE2bNmXt2rU7XPNudXV1GTBgQIcNAAAAStOpj8efe+65ufXWW3f5h1cqlVxyySV54IEH8vjjj2fEiBEdnh8xYkQaGxuzcOHC6r5NmzZl0aJFGTt2bJJk9OjR6d27d4c1zc3NefbZZ6trAAAAYE/UqS+i27RpU/7P//k/WbhwYcaMGZN+/fp1eH727Nnv6X0uvvji3HPPPfnHf/zH9O/fv3pFvb6+Pn379k1NTU2mTJmSmTNnZuTIkRk5cmRmzpyZffbZJ+ecc0517eTJkzNt2rQMGjQoAwcOzPTp03PIIYdk/PjxnTk8AAAAKMJORftLL72Uj370o3n22Wdz+OGHJ0l++ctfdlizMx+bnzt3bpLkmGOO6bD/tttuy/nnn58kufzyy7Nx48ZcdNFFWbt2bY444og8+uij6d+/f3X9TTfdlNra2px55pnZuHFjxo0bl3nz5qVXr147c3gAAABQlJ36Pe29evVKc3NzhgwZkiQ566yz8u1vf3uH947vKfyedgAAgDL4Pe0d7dQ97e/u+x/84AfZsGFD5yYEAAAAfq9OfRHdO3biIj0AAACwk3Yq2mtqara5Z70rf/UbAAAA8B926ovoKpVKzj///NTV1SVJ3nrrrVx44YXbfHv8Aw880HUTAgAAwF5qp6L9vPPO6/D4L/7iL7p0GAAAAOA/7FS033bbbd01BwAAAPAuu/RFdAAAAED3Ee0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIXq0Wj/yU9+kpNPPjlNTU2pqanJ97///Q7Pn3/++ampqemwffKTn+ywpr29PZdeemkGDx6cfv365ZRTTsnq1at341EAAABA9+jRaN+wYUMOO+ywzJkzZ4drPvvZz6a5ubm6Pfzwwx2enzJlShYsWJD58+dn8eLFWb9+fU466aRs2bKlu8cHAACAblXbkz980qRJmTRp0u9dU1dXl8bGxu0+19ramltvvTV33nlnxo8fnyS56667MmzYsDz22GM5/vjju3xmAAAA2F2Kv6f9iSeeyJAhQ3LggQfmK1/5StasWVN9bvny5dm8eXMmTpxY3dfU1JRRo0ZlyZIlO3zP9vb2tLW1ddgAAACgNEVH+6RJk3L33Xfn8ccfz4033pilS5fmuOOOS3t7e5KkpaUlffr0yb777tvhdQ0NDWlpadnh+86aNSv19fXVbdiwYd16HAAAANAZPfrx+D/krLPOqv7nUaNGZcyYMRk+fHgeeuihnH766Tt8XaVSSU1NzQ6fv/LKKzN16tTq47a2NuEOAABAcYq+0v5uQ4cOzfDhw/PCCy8kSRobG7Np06asXbu2w7o1a9akoaFhh+9TV1eXAQMGdNgAAACgNHtUtL/xxhtZtWpVhg4dmiQZPXp0evfunYULF1bXNDc359lnn83YsWN7akwAAADoEj368fj169fnX//1X6uPV65cmRUrVmTgwIEZOHBgZsyYkTPOOCNDhw7Nyy+/nKuuuiqDBw/OaaedliSpr6/P5MmTM23atAwaNCgDBw7M9OnTc8ghh1S/TR4AAAD2VD0a7cuWLcuxxx5bffzOfebnnXde5s6dm2eeeSZ33HFH3nzzzQwdOjTHHnts7rvvvvTv37/6mptuuim1tbU588wzs3HjxowbNy7z5s1Lr169dvvxAAAAQFeqqVQqlZ4eoqe1tbWlvr4+ra2t7m8HAADoQR+94qFdfo+Xrz2xCybpXu+1Q/eoe9oBAABgbyLaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUD0a7T/5yU9y8sknp6mpKTU1Nfn+97/f4flKpZIZM2akqakpffv2zTHHHJPnnnuuw5r29vZceumlGTx4cPr165dTTjklq1ev3o1HAQAAAN2jR6N9w4YNOeywwzJnzpztPn/99ddn9uzZmTNnTpYuXZrGxsZMmDAh69atq66ZMmVKFixYkPnz52fx4sVZv359TjrppGzZsmV3HQYAAAB0i9qe/OGTJk3KpEmTtvtcpVLJzTffnKuvvjqnn356kuT2229PQ0ND7rnnnlxwwQVpbW3NrbfemjvvvDPjx49Pktx1110ZNmxYHnvssRx//PG77VgAAACgqxV7T/vKlSvT0tKSiRMnVvfV1dXl6KOPzpIlS5Iky5cvz+bNmzusaWpqyqhRo6prtqe9vT1tbW0dNgAAAChNsdHe0tKSJGloaOiwv6GhofpcS0tL+vTpk3333XeHa7Zn1qxZqa+vr27Dhg3r4ukBAABg1xUb7e+oqanp8LhSqWyz793+0Jorr7wyra2t1W3VqlVdMisAAAB0pWKjvbGxMUm2uWK+Zs2a6tX3xsbGbNq0KWvXrt3hmu2pq6vLgAEDOmwAAABQmmKjfcSIEWlsbMzChQur+zZt2pRFixZl7NixSZLRo0end+/eHdY0Nzfn2Wefra4BAACAPVWPfnv8+vXr86//+q/VxytXrsyKFSsycODAfOQjH8mUKVMyc+bMjBw5MiNHjszMmTOzzz775JxzzkmS1NfXZ/LkyZk2bVoGDRqUgQMHZvr06TnkkEOq3yYPAAAAe6oejfZly5bl2GOPrT6eOnVqkuS8887LvHnzcvnll2fjxo256KKLsnbt2hxxxBF59NFH079//+prbrrpptTW1ubMM8/Mxo0bM27cuMybNy+9evXa7ccDAAAAXammUqlUenqIntbW1pb6+vq0tra6vx0AAKAHffSKh3b5PV6+9sQumKR7vdcOLfaedgAAANjbiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAAChU0dE+Y8aM1NTUdNgaGxurz1cqlcyYMSNNTU3p27dvjjnmmDz33HM9ODEAAAB0naKjPUkOPvjgNDc3V7dnnnmm+tz111+f2bNnZ86cOVm6dGkaGxszYcKErFu3rgcnBgAAgK5RfLTX1tamsbGxuu23335JfneV/eabb87VV1+d008/PaNGjcrtt9+e3/72t7nnnnt6eGoAAADYdcVH+wsvvJCmpqaMGDEiZ599dl566aUkycqVK9PS0pKJEydW19bV1eXoo4/OkiVLfu97tre3p62trcMGAAAApSk62o844ojccccd+eEPf5i/+7u/S0tLS8aOHZs33ngjLS0tSZKGhoYOr2loaKg+tyOzZs1KfX19dRs2bFi3HQMAAAB0VtHRPmnSpJxxxhk55JBDMn78+Dz00ENJkttvv726pqampsNrKpXKNvve7corr0xra2t1W7VqVdcPDwAAALuo6Gh/t379+uWQQw7JCy+8UP0W+XdfVV+zZs02V9/fra6uLgMGDOiwAQAAQGn2qGhvb2/Pv/zLv2To0KEZMWJEGhsbs3DhwurzmzZtyqJFizJ27NgenBIAAAC6Rm1PD/D7TJ8+PSeffHI+8pGPZM2aNbnmmmvS1taW8847LzU1NZkyZUpmzpyZkSNHZuTIkZk5c2b22WefnHPOOT09OgAAAOyyoqN99erV+W//7b/l9ddfz3777ZdPfvKTefLJJzN8+PAkyeWXX56NGzfmoosuytq1a3PEEUfk0UcfTf/+/Xt4cgAAANh1NZVKpdLTQ/S0tra21NfXp7W11f3tAAAAPeijVzy0y+/x8rUndsEk3eu9dugedU87AAAA7E1EOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChant6AAAA2JGPXvHQLr/Hy9ee2AWTAPQMV9oBAACgUKIdAAAACiXaAQAAoFDuaQcA+Hfun6Y7+d8X0BmutAMAAEChXGkHAHqcK5B0J//7Kk9X/DPpCv65sicQ7QDsNrv6hzR/uAIA9jY+Hg8AAACFcqUdgPekhI8y+ohr1yvhn2tXeT8dCwC8Q7QDAAA7xV+Swe7j4/EAAABQKFfaAQAKUsoVTLeSlKmU/328X7jtij2BaAeAPZQ/vAP0POFPdxPtAOxV/OEK6Ax/SQb0FPe0AwAAQKFcaQfoRqVc1XWFCHYf5xuws0r58wJlEu1AcfyLC6Dn+csH2LP489P7l4/HAwAAQKFcaQe61Pvpykwpx1LKHPwHVzMAgN1FtEMhdjUCBAAAALz/1FQqlUpPD9EVbrnlltxwww1pbm7OwQcfnJtvvjlHHXXUe3ptW1tb6uvr09ramgEDBnTzpLwfvV+uhPrCMwAA3g/2hAta77VD3xf3tN93332ZMmVKrr766jz11FM56qijMmnSpLz66qs9PRoAAAB02vviSvsRRxyRww8/PHPnzq3u+/jHP55TTz01s2bN2mZ9e3t72tvbq49bW1vzkY98JKtWrSr+Svuob/xwl9/j2W8e3wWTlKEr/vsAAADeX/aE5mlra8uwYcPy5ptvpr6+fofr9vh72jdt2pTly5fniiuu6LB/4sSJWbJkyXZfM2vWrHzzm9/cZv+wYcO6ZcbS1N/c0xMAAAB0nz2pedatW/f+jvbXX389W7ZsSUNDQ4f9DQ0NaWlp2e5rrrzyykydOrX6eOvWrfnNb36TQYMGpaamplvnZVvv/A3TnvBJB9hdnBewLecFbMt5AdvaU86LSqWSdevWpamp6feu2+Oj/R3vju1KpbLDAK+rq0tdXV2HfR/+8Ie7azTeowEDBhR9UkFPcF7AtpwXsC3nBWxrTzgvft8V9nfs8V9EN3jw4PTq1Wubq+pr1qzZ5uo7AAAA7En2+Gjv06dPRo8enYULF3bYv3DhwowdO7aHpgIAAIBd9774ePzUqVPzhS98IWPGjMmRRx6Zv/3bv82rr76aCy+8sKdH4z2oq6vLN77xjW1uWYC9mfMCtuW8gG05L2Bb77fz4n3xK9+S5JZbbsn111+f5ubmjBo1KjfddFM+85nP9PRYAAAA0Gnvm2gHAACA95s9/p52AAAAeL8S7QAAAFAo0Q4AAACFEu0AAABQKNHObnHLLbdkxIgR+eAHP5jRo0fnn/7pn3a49oEHHsiECROy3377ZcCAATnyyCPzwx/+cDdOC7vHzpwX/9lPf/rT1NbW5r/8l//SvQNCD9jZ86K9vT1XX311hg8fnrq6uvzJn/xJ/u///b+7aVrYPXb2vLj77rtz2GGHZZ999snQoUPzxS9+MW+88cZumha6109+8pOcfPLJaWpqSk1NTb7//e//wdcsWrQoo0ePzgc/+MH88R//cb7zne90/6BdSLTT7e67775MmTIlV199dZ566qkcddRRmTRpUl599dXtrv/JT36SCRMm5OGHH87y5ctz7LHH5uSTT85TTz21myeH7rOz58U7Wltbc+6552bcuHG7aVLYfTpzXpx55pn50Y9+lFtvvTXPP/987r333vzpn/7pbpwautfOnheLFy/Oueeem8mTJ+e5557L9773vSxdujRf/vKXd/Pk0D02bNiQww47LHPmzHlP61euXJkTTjghRx11VJ566qlcddVV+epXv5p/+Id/6OZJu45f+Ua3O+KII3L44Ydn7ty51X0f//jHc+qpp2bWrFnv6T0OPvjgnHXWWfnLv/zL7hoTdqvOnhdnn312Ro4cmV69euX73/9+VqxYsRumhd1jZ8+LRx55JGeffXZeeumlDBw4cHeOCrvNzp4X//t//+/MnTs3L774YnXfX//1X+f666/PqlWrdsvMsLvU1NRkwYIFOfXUU3e45utf/3oefPDB/Mu//Et134UXXpif//zn+ed//ufdMOWuc6WdbrVp06YsX748EydO7LB/4sSJWbJkyXt6j61bt2bdunX+QMb7RmfPi9tuuy0vvvhivvGNb3T3iLDbdea8ePDBBzNmzJhcf/31+aM/+qMceOCBmT59ejZu3Lg7RoZu15nzYuzYsVm9enUefvjhVCqV/PrXv87999+fE088cXeMDMX553/+523OoeOPPz7Lli3L5s2be2iqnVPb0wPw/vb6669ny5YtaWho6LC/oaEhLS0t7+k9brzxxmzYsCFnnnlmd4wIu11nzosXXnghV1xxRf7pn/4ptbX+r5v3n86cFy+99FIWL16cD37wg1mwYEFef/31XHTRRfnNb37jvnbeFzpzXowdOzZ33313zjrrrLz11lt5++23c8opp+Sv//qvd8fIUJyWlpbtnkNvv/12Xn/99QwdOrSHJnvvXGlnt6ipqenwuFKpbLNve+69997MmDEj9913X4YMGdJd40GPeK/nxZYtW3LOOefkm9/8Zg488MDdNR70iJ3598XWrVtTU1OTu+++O5/4xCdywgknZPbs2Zk3b56r7byv7Mx58Ytf/CJf/epX85d/+ZdZvnx5HnnkkaxcuTIXXnjh7hgVirS9c2h7+0vlcg3davDgwenVq9c2fxu8Zs2abf7G693uu+++TJ48Od/73vcyfvz47hwTdqudPS/WrVuXZcuW5amnnsoll1yS5HexUqlUUltbm0cffTTHHXfcbpkduktn/n0xdOjQ/NEf/VHq6+ur+z7+8Y+nUqlk9erVGTlyZLfODN2tM+fFrFmz8qlPfSr/43/8jyTJoYcemn79+uWoo47KNddcs0dcVYSu1NjYuN1zqLa2NoMGDeqhqXaOK+10qz59+mT06NFZuHBhh/0LFy7M2LFjd/i6e++9N+eff37uuece92DxvrOz58WAAQPyzDPPZMWKFdXtwgsvzMc+9rGsWLEiRxxxxO4aHbpNZ/598alPfSqvvfZa1q9fX933y1/+Mh/4wAey//77d+u8sDt05rz47W9/mw98oOMf8Xv16pXkP64uwt7kyCOP3OYcevTRRzNmzJj07t27h6baSRXoZvPnz6/07t27cuutt1Z+8YtfVKZMmVLp169f5eWXX65UKpXKFVdcUfnCF75QXX/PPfdUamtrK3/zN39TaW5urm5vvvlmTx0CdLmdPS/e7Rvf+EblsMMO203Twu6xs+fFunXrKvvvv3/lv/7X/1p57rnnKosWLaqMHDmy8uUvf7mnDgG63M6eF7fddlultra2csstt1RefPHFyuLFiytjxoypfOITn+ipQ4AutW7duspTTz1VeeqppypJKrNnz6489dRTlVdeeaVSqWx7Trz00kuVffbZp/K1r32t8otf/KJy6623Vnr37l25//77e+oQdpqPx9PtzjrrrLzxxhv5n//zf6a5uTmjRo3Kww8/nOHDhydJmpubO/yu0e9+97t5++23c/HFF+fiiy+u7j/vvPMyb9683T0+dIudPS9gb7Cz58WHPvShLFy4MJdeemnGjBmTQYMG5cwzz8w111zTU4cAXW5nz4vzzz8/69aty5w5czJt2rR8+MMfznHHHZfrrruupw4ButSyZcty7LHHVh9PnTo1yX+0wrvPiREjRuThhx/O1772tfzN3/xNmpqa8u1vfztnnHHGbp+9s/yedgAAACiUe9oBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQ/x9bSX6bLIynsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "community_context_df['density'].plot.hist(bins=50, figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb83c40",
   "metadata": {},
   "source": [
    "#### Investigate Communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "960ade35",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_author_papers(df, G):\n",
    "    \n",
    "    G = G.copy()\n",
    "    \n",
    "    check_nodes = G.nodes\n",
    "\n",
    "    return sorted(df[df['authors'].isin(check_nodes)]['title'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b5ec685",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_communities(community_context_df, min_density, max_density, max_nodes=None):\n",
    "    \n",
    "    if max_nodes==None:\n",
    "        community_context_df['node_count'].sum()\n",
    "    \n",
    "    community_context_df = community_context_df.copy()\n",
    "\n",
    "    mask = (community_context_df['density']>min_density) \\\n",
    "        & (community_context_df['density']<max_density)  \\\n",
    "        & (community_context_df['node_count']<max_nodes)\n",
    "    \n",
    "    community_context_df = community_context_df[mask].copy()\n",
    "    community_context_df = community_context_df.sample(10) # grabbing ten to see the ranges\n",
    "\n",
    "    community_context_df.sort_values('density', inplace=True)\n",
    "    communities = community_context_df['community'].values[0:10]\n",
    "    \n",
    "    return communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37cedc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_community(G_check):\n",
    "\n",
    "    print('Density: {}'.format(nx.density(G_check)))\n",
    "    print('Node Count: {}'.format(len(G_check.nodes)))\n",
    "    return draw_graph(G_check, show_names=True, node_size=3, edge_width=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4cd8b228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6211,  3744,   767, 10365,  1648,  3789,   457,  5883,  3469,\n",
       "       10292], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_density = 0.1\n",
    "max_density = 0.3\n",
    "max_nodes = 100\n",
    "\n",
    "communities = fetch_communities(community_context_df, min_density=min_density, max_density=max_density, max_nodes=max_nodes)\n",
    "communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b86e905b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.1453373015873016\n",
      "Node Count: 64\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"900.0\" height=\"540\">\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 252 252 292 210\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 98 341\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 334 281\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 406 330\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 83 169\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 71 282\"/>\n",
       "<path stroke-width=\"0.2\" stroke=\"gray\" d=\"M 218 253 20 181\"/>\n",
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       "<text text-anchor=\"start\" x=\"258\" y=\"252\" font-size=\"10\">Shaohan Feng</text><text text-anchor=\"start\" x=\"233\" y=\"80\" font-size=\"10\">Tianxu Li</text><text text-anchor=\"start\" x=\"298\" y=\"210\" font-size=\"10\">Xiao Lu</text><text text-anchor=\"start\" x=\"454\" y=\"474\" font-size=\"10\">Narisa Zhao</text><text text-anchor=\"start\" x=\"333\" y=\"520\" font-size=\"10\">Napat Ngoenriang</text><text text-anchor=\"start\" x=\"604\" y=\"397\" font-size=\"10\">Boon Hee Soong</text><text text-anchor=\"start\" x=\"406\" y=\"77\" font-size=\"10\">Emilio Calvanese Strinati</text><text text-anchor=\"start\" x=\"78\" y=\"314\" font-size=\"10\">Khaled Elbassioni</text><text text-anchor=\"start\" x=\"235\" y=\"133\" font-size=\"10\">Cyril Leung</text><text text-anchor=\"start\" x=\"399\" y=\"271\" font-size=\"10\">Ping Wang</text><text text-anchor=\"start\" x=\"352\" y=\"251\" font-size=\"10\">Chunyan Miao</text><text text-anchor=\"start\" x=\"83\" y=\"206\" font-size=\"10\">Feng Shaohan</text><text text-anchor=\"start\" x=\"637\" y=\"215\" font-size=\"10\">Jer Shyuan Ng</text><text text-anchor=\"start\" x=\"654\" y=\"358\" font-size=\"10\">Kongrath Suankaewmanee</text><text text-anchor=\"start\" x=\"450\" y=\"212\" font-size=\"10\">Fen Hou</text><text text-anchor=\"start\" x=\"238\" y=\"420\" font-size=\"10\">Tran The Anh</text><text text-anchor=\"start\" x=\"287\" y=\"279\" font-size=\"10\">Dong In Kim</text><text text-anchor=\"start\" x=\"285\" y=\"87\" font-size=\"10\">Bing Chen</text><text text-anchor=\"start\" x=\"726\" y=\"294\" font-size=\"10\">Andres Kwasinski</text><text text-anchor=\"start\" x=\"326\" y=\"187\" font-size=\"10\">Dinh Thai Hoang</text><text text-anchor=\"start\" x=\"380\" y=\"334\" font-size=\"10\">Wenbo Wang</text><text text-anchor=\"start\" x=\"398\" y=\"204\" font-size=\"10\">Yang Zhang</text><text text-anchor=\"start\" x=\"305\" y=\"315\" font-size=\"10\">Ying-Chang Liang</text><text text-anchor=\"start\" x=\"182\" y=\"323\" font-size=\"10\">Zhengwei Ni</text><text text-anchor=\"start\" x=\"558\" y=\"303\" font-size=\"10\">Jiangtian Nie</text><text text-anchor=\"start\" x=\"323\" y=\"344\" font-size=\"10\">Sumei Sun</text><text text-anchor=\"start\" x=\"197\" y=\"174\" font-size=\"10\">Kun Zhu</text><text text-anchor=\"start\" x=\"104\" y=\"341\" font-size=\"10\">Tien Hoa Nguyen</text><text text-anchor=\"start\" x=\"340\" y=\"281\" font-size=\"10\">Dusit Niyato</text><text text-anchor=\"start\" x=\"154\" y=\"165\" font-size=\"10\">Sherman Xuemin Shen</text><text text-anchor=\"start\" x=\"533\" y=\"255\" font-size=\"10\">Wei Yang Bryan Lim</text><text text-anchor=\"start\" x=\"174\" y=\"469\" font-size=\"10\">Xinyuan Zhang</text><text text-anchor=\"start\" x=\"412\" y=\"330\" font-size=\"10\">Zehui Xiong</text><text text-anchor=\"start\" x=\"224\" y=\"253\" font-size=\"10\">Nguyen Cong Luong</text><text text-anchor=\"start\" x=\"89\" y=\"169\" font-size=\"10\">Huy T. 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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_check = community_context_dict[communities[0]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17a5d5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A Contract-based Incentive Mechanism for Energy Harvesting-based Internet of Things',\n",
       " 'A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Network',\n",
       " 'A Distributed Merge and Split Algorithm for Fair Cooperation in Wireless Networks',\n",
       " 'A Dynamic Hierarchical Framework for IoT-assisted Metaverse Synchronization',\n",
       " 'A Layered Coalitional Game Framework of Wireless Relay Network',\n",
       " 'A Learning-Based Coexistence Mechanism for LAA-LTE Based HetNets',\n",
       " 'A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones',\n",
       " 'A Real-time Group Auction System for Efficient Allocation of Cloud Internet Applications',\n",
       " 'A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market',\n",
       " 'A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing (Online report)',\n",
       " 'A Survey on Applications of Game Theory in Blockchain',\n",
       " 'A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications',\n",
       " 'A VCG-based Fair Incentive Mechanism for Federated Learning',\n",
       " 'A game theory approach for self-coexistence analysis among IEEE 802.22 networks',\n",
       " 'AI-Generated Network Design: A Diffusion Model-based Learning Approach',\n",
       " 'AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues',\n",
       " 'Achieving Covert Communication in Large-Scale SWIPT-Enabled D2D Networks',\n",
       " 'Achieving Deterministic Service in Mobile Edge Computing (MEC) Networks',\n",
       " 'AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks',\n",
       " 'An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks',\n",
       " 'An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach',\n",
       " 'Analysis of the Game-Theoretic Modeling of Backscatter Wireless Sensor Networks under Smart Interference',\n",
       " 'Analyzing and Visualizing Scalar Fields on Graphs',\n",
       " 'Applications of Deep Reinforcement Learning in Communications and Networking: A Survey',\n",
       " 'Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey',\n",
       " 'Applications of Economic and Pricing Models for Wireless Network Security: A Survey',\n",
       " 'Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey',\n",
       " 'Applications of Repeated Games in Wireless Networks: A Survey',\n",
       " 'Artificial Intelligence-Enabled Intelligent 6G Networks',\n",
       " 'Asymptotically Optimal Distributed Channel Allocation: a Competitive Game-Theoretic Approach',\n",
       " 'Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks',\n",
       " 'Boundary value problems in consensus networks',\n",
       " 'CdSe-Au nanorod networks welded by gold domains - a promising structure for nano-optoelectronic components',\n",
       " 'Cloud/fog computing resource management and pricing for blockchain networks',\n",
       " 'Coalition Formation Games for Collaborative Spectrum Sensing',\n",
       " 'Coalition Formation Games for Distributed Cooperation Among Roadside Units in Vehicular Networks',\n",
       " 'Coalition Games with Cooperative Transmission: A Cure for the Curse of Boundary Nodes in Selfish Packet-Forwarding Wireless Networks',\n",
       " 'Coalitional Game Theoretic Approach for Cooperative Transmission in Vehicular Networks',\n",
       " 'Coalitional Game Theory for Communication Networks: A Tutorial',\n",
       " 'Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks',\n",
       " 'Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks',\n",
       " 'Coalitional Graph Games for Popular Content Distribution in Cognitive Radio VANETs',\n",
       " 'Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning',\n",
       " 'Collaborative Smartphone Sensing using Overlapping Coalition Formation Games',\n",
       " 'Competition and Cooperation Analysis for Data Sponsored Market: A Network Effects Model',\n",
       " 'Competitive Spectrum Management with Incomplete Information',\n",
       " 'Complex Network Theoretical Analysis on Information Dissemination over Vehicular Networks',\n",
       " 'Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game',\n",
       " 'Computing Power Network: A Survey',\n",
       " 'Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching',\n",
       " 'Congestion-Aware Distributed Network Selection for Integrated Cellular and Wi-Fi Networks',\n",
       " 'Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain',\n",
       " 'Correlation between Fragility and the Arrhenius Crossover Phenomenon in Metallic, Molecular, and Network Liquids',\n",
       " 'Coverage and Rate Analysis for Co-Existing RF/VLC Downlink Cellular Networks',\n",
       " 'DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks',\n",
       " 'Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey',\n",
       " 'Data Offloading in Load Coupled Networks: A Utility Maximization Framework',\n",
       " 'Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks',\n",
       " 'Dense networks with scale-free feature',\n",
       " 'Dirac nodal lines and induced spin Hall effect in metallic rutile oxides',\n",
       " 'Distributed Cooperative Sensing in Cognitive Radio Networks: An Overlapping Coalition Formation Approach',\n",
       " 'Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks',\n",
       " 'Distributed Resource Allocation for Network Slicing of Bandwidth and Computational Resource',\n",
       " 'Distributed Resource Allocation in D2D-Enabled Multi-tier Cellular Networks: An Auction Approach',\n",
       " 'Distributed Time-Sensitive Task Selection in Mobile Crowdsensing',\n",
       " 'Distributed User Association in Energy Harvesting Small Cell Networks: A Competitive Market Model with Uncertainty',\n",
       " 'Dual Auction Mechanism for Transaction Forwarding and Validation in Complex Wireless Blockchain Network',\n",
       " 'Dynamic Interference Minimization Routing Game for On-Demand Cognitive Pilot Channel',\n",
       " 'Dynamic Network Service Selection in IRS-Assisted Wireless Networks: A Game Theory Approach',\n",
       " 'Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches',\n",
       " 'Dynamic Popular Content Distribution in Vehicular Networks using Coalition Formation Games',\n",
       " 'Dynamic Pricing for Revenue Maximization in Mobile Social Data Market with Network Effects',\n",
       " 'Dynamics in Coded Edge Computing for IoT: A Fractional Evolutionary Game Approach',\n",
       " 'Economics of Semantic Communication System: An Auction Approach',\n",
       " 'Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach',\n",
       " 'Evolutionary Game for Consensus Provision in Permissionless Blockchain Networks with Shard',\n",
       " 'Evolutionary Game for Mining Pool Selection in Blockchain Networks',\n",
       " 'Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey',\n",
       " 'FedChain: Secure Proof-of-Stake-based Framework for Federated-blockchain Systems',\n",
       " 'Field Experiment on a Robust Hierarchical Metropolitan Quantum Cryptography Network',\n",
       " 'Field Experimental \"Star Type\" Metropolitan Quantum Key Distribution Network',\n",
       " 'From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks',\n",
       " 'Game Theoretic Analysis for Joint Sponsored and Edge Caching Content Service Market',\n",
       " 'Game Theoretic Approaches to Massive Data Processing in Wireless Networks',\n",
       " 'Game Theoretic Dynamic Channel Allocation for Frequency-Selective Interference Channels',\n",
       " 'Game Theoretic Methods for the Smart Grid',\n",
       " 'Handshake between Fibonacci series and pure preferential attachment mechanism on a graph-model',\n",
       " 'Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents',\n",
       " 'Hierarchic Power Allocation for Spectrum Sharing in OFDM-Based Cognitive Radio Networks',\n",
       " 'Hierarchical Cooperation for Operator-Controlled Device-to-Device Communications: A Layered Coalitional Game Approach',\n",
       " 'Hybrid deep neural network based prediction method for unsteady flows with moving boundaries',\n",
       " 'Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach',\n",
       " 'Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning',\n",
       " 'Integrated Resource Management in Software Defined Networking, Caching and Computing',\n",
       " 'Joint Rate Allocation and Power Control for RSMA-Based Communication and Radar Coexistence Systems',\n",
       " 'Joint Service Pricing and Cooperative Relay Communication for Federated Learning',\n",
       " 'Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue',\n",
       " 'Large-scale Deterministic Transmission among IEEE 802.1Qbv Time-Sensitive Networks',\n",
       " 'Learning Semantics: An Opportunity for Effective 6G Communications',\n",
       " 'Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges',\n",
       " 'Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks',\n",
       " 'Minority Games With Applications to Distributed Decision Making and Control in Wireless Networks',\n",
       " 'Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning',\n",
       " 'Mobility-Aware Analysis of 5G and B5G Cellular Networks: A Tutorial',\n",
       " 'Multi-Block ADMM for Big Data Optimization in Smart Grid',\n",
       " 'Multi-operator spectrum sharing using matching game in small cells network',\n",
       " 'Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects',\n",
       " 'Non-Cash Auction for Spectrum Trading in Cognitive Radio Networks: A Contract Theoretical Model with Joint Adverse Selection and Moral Hazard',\n",
       " 'Non-atomic Games for Multi-User Systems',\n",
       " 'Non-cooperative Feedback Rate Control Game for Channel State Information in Wireless Networks',\n",
       " 'On Coordination of Smart Grid and Cooperative Cloud Providers',\n",
       " 'On Cyber Risk Management of Blockchain Networks: A Game Theoretic Approach',\n",
       " 'Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach',\n",
       " 'Optimal Cross Slice Orchestration for 5G Mobile Services',\n",
       " 'Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain',\n",
       " 'Optimal Stochastic Coded Computation Offloading in Unmanned Aerial Vehicles Network',\n",
       " 'Overlapping Coalition Formation Games for Emerging Communication Networks',\n",
       " 'PINO-MBD: Physics-informed Neural Operator for Solving Coupled ODEs in Multi-body Dynamics',\n",
       " 'Payoff Allocation of Service Coalition in Wireless Mesh Network: A Cooperative Game Perspective',\n",
       " 'Physical Layer Security: Coalitional Games for Distributed Cooperation',\n",
       " 'Power allocation with stackelberg game in femtocell networks: a self-learning approach',\n",
       " 'Price of Anarchy in Algorithmic Matching of Romantic Partners',\n",
       " 'Providing Long-Term Participation Incentive in Participatory Sensing',\n",
       " 'Quantifying Inefficiency of Fair Cost-Sharing Mechanisms for Sharing Economy',\n",
       " 'Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul',\n",
       " 'Resource Allocation in Full-Duplex Communications for Future Wireless Networks',\n",
       " 'Resource Allocation in Wireless Powered Relay Networks: A Bargaining Game Approach',\n",
       " 'Resource Management for Device-to-Device Underlay Communication',\n",
       " 'Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey',\n",
       " 'Retrospective Interference Regeneration Schemes for Relay-Aided K-user MIMO Downlink Networks',\n",
       " 'Semantic Information Market For The Metaverse: An Auction Based Approach',\n",
       " 'Single Controller Stochastic Games for Optimized Moving Target Defense',\n",
       " 'Smart Data Pricing Models for Internet-of-Things (IoT): A Bundling Strategy Approach',\n",
       " 'Social Data Offloading in D2D-Enhanced Cellular Networks by Network Formation Games',\n",
       " 'Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks',\n",
       " 'Stochastic Resource Allocation for Semantic Communication-aided Virtual Transportation Networks in the Metaverse',\n",
       " 'Task Allocation and Mobile Base Station Deployment in Wireless Powered Spatial Crowdsourcing',\n",
       " 'Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)',\n",
       " 'Tiered cloud storage via two-stage, latency-aware bidding',\n",
       " 'Toward an Automated Auction Framework for Wireless Federated Learning Services Market',\n",
       " 'Trapping problem on star-type graphs with applications',\n",
       " 'Truthful Mechanisms for Secure Communication in Wireless Cooperative System',\n",
       " 'Truthful Spectrum Auction for Efficient Anti-Jamming in Cognitive Radio Networks',\n",
       " 'Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home',\n",
       " 'Virtualization of 5G Cellular Networks as a Hierarchical Combinatorial Auction',\n",
       " 'Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "show_author_papers(df, G_check)"
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     "execution_count": 25,
     "metadata": {},
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    }
   ],
   "source": [
    "G_check = community_context_dict[communities[1]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6a3082a",
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     "data": {
      "text/plain": [
       "['A comparative analysis of knowledge acquisition performance in complex networks',\n",
       " 'A comparative analysis of local network similarity measurements: application to author citation networks',\n",
       " 'A machine learning approach to predicting dynamical observables from network structure',\n",
       " 'Analyzing Trails in Complex Networks',\n",
       " 'Analyzing complex networks through correlations in centrality measurements',\n",
       " 'Automatic Network Fingerprinting through Single-Node Motifs',\n",
       " 'Border trees of complex networks',\n",
       " 'Chain motifs: The tails and handles of complex networks',\n",
       " 'Characterization and space embedding of directed graphs and social networks through magnetic Laplacians',\n",
       " 'Characterizing Width Uniformity by Wave Propagation',\n",
       " 'City Motifs as Revealed by Similarity Between Hierarchical Features',\n",
       " 'Comparing the writing style of real and artificial papers',\n",
       " 'Complex Networks of Functions',\n",
       " 'Complex systems: features, similarity and connectivity',\n",
       " 'Connecting Network Science and Information Theory',\n",
       " 'Distance-Based Network Partitioning',\n",
       " 'Effective networks for real-time distributed processing',\n",
       " 'Enzyme Similarity Networks',\n",
       " 'Fundamentals of spreading processes in single and multilayer complex networks',\n",
       " 'Hierarchical Spatial Organization of Geographical Networks',\n",
       " 'How Coupled are Mass Spectrometry and Capillary Electrophoresis?',\n",
       " 'How Integrated are Theoretical and Applied Physics?',\n",
       " 'Interdisciplinary Relationships Between Biological and Physical Sciences',\n",
       " 'Knowledge Acquisition: A Complex Networks Approach',\n",
       " 'Language Networks: a Practical Approach',\n",
       " 'Layer degradation triggers an abrupt structural transition in multiplex networks',\n",
       " 'Machine learning for the prediction of voter model opinions through complex network structures',\n",
       " 'Mean-field theory of vector spin models on networks with arbitrary degree distributions',\n",
       " 'Modeling the Perspectives for Scientific Advancement',\n",
       " 'Modular Dynamics of Financial Market Networks',\n",
       " 'Note: Distance-Based Network Partitioning',\n",
       " 'On Hypercomplex Networks',\n",
       " 'Spacing ratio characterization of the spectra of directed random networks',\n",
       " 'Spectra of random networks in the weak clustering regime',\n",
       " 'Surviving opinions in Sznajd models on complex networks',\n",
       " 'Text characterization based on recurrence networks',\n",
       " 'The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks',\n",
       " 'The Kuramoto model in complex networks',\n",
       " 'Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks',\n",
       " 'Universality in the spectral and eigenfunction properties of random networks',\n",
       " 'Using network science and text analytics to produce surveys in a scientific topic',\n",
       " 'Word sense disambiguation: a complex network approach']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "359b8f0e",
   "metadata": {},
   "outputs": [
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      "Node Count: 29\n"
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   "source": [
    "G_check = community_context_dict[communities[2]]['G']\n",
    "draw_community(G_check)"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f677b9c8",
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    {
     "data": {
      "text/plain": [
       "['A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks',\n",
       " 'A TTL-based Approach for Content Placement in Edge Networks',\n",
       " 'A marine radioisotope gamma-ray spectrum analysis method based on Monte Carlo simulation and MLP neural network',\n",
       " 'An Exploratory Study of Stock Price Movements from Earnings Calls',\n",
       " 'Broadcast Analysis for Large Cooperative Wireless Networks',\n",
       " 'Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions',\n",
       " 'Information Cascades in Feed-based Networks of Users with Limited Attention',\n",
       " 'Learnable Digital Twin for Efficient Wireless Network Evaluation',\n",
       " 'Learning Features of Network Structures Using Graphlets',\n",
       " 'Learning to Generate Networks',\n",
       " 'MON: Mission-optimized Overlay Networks',\n",
       " 'Maximizing Coverage Centrality via Network Design: Extended Version',\n",
       " 'Network Classification in Temporal Networks Using Motifs',\n",
       " 'Network Robustness via Global k-cores',\n",
       " 'Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks',\n",
       " 'On a Class of Stochastic Multilayer Networks',\n",
       " 'On the Characterization of Quantum Flip Stars with Quantum Network Tomography',\n",
       " 'On the Connectivity and Multihop Delay of Ad Hoc Cognitive Radio Networks',\n",
       " 'On the Performance Analysis of Epidemic Routing in Non-Sparse Delay Tolerant Networks',\n",
       " 'Outlier Detection from Network Data with Subnetwork Interpretation',\n",
       " 'Percolation Thresholds for Robust Network Connectivity',\n",
       " 'Quantum Network Planning for Utility Maximization',\n",
       " 'Quantum Network Utility: A Framework for Benchmarking Quantum Networks',\n",
       " 'Quantum Networks For Open Science',\n",
       " 'Resource Sharing in the Edge: A Distributed Bargaining-Theoretic Approach',\n",
       " 'Spatial characterization of photonic polarization entanglement using a Tpx3Cam intensified fast-camera',\n",
       " 'To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?',\n",
       " 'Universal Quantum Walk Control Plane for Quantum Networks',\n",
       " 'Wireless Network Coding with Imperfect Overhearing']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9ca31eed",
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "Density: 0.25925925925925924\n",
      "Node Count: 27\n"
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   "source": [
    "G_check = community_context_dict[communities[3]]['G']\n",
    "draw_community(G_check)"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a9b992e",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['$k$-core percolation on complex networks: Comparing random, localized and targeted attacks',\n",
       " 'Altering control modes of complex networks based on edge removal',\n",
       " 'Assortativity Decreases the Robustness of Interdependent Networks',\n",
       " 'Avalanches in Breakdown and Fracture Processes',\n",
       " 'Betweenness Centrality of Fractal and Non-Fractal Scale-Free Model Networks and Tests on Real Networks',\n",
       " 'Cascade of failures in coupled network systems with multiple support-dependent relations',\n",
       " 'Cascading Failures in Complex Networks',\n",
       " 'Catastrophic cascade of failures in interdependent networks',\n",
       " 'Classes of behavior of small-world networks',\n",
       " 'Complementarity in Complex Networks',\n",
       " 'Conditions for viral influence spreading through multiplex correlated social networks',\n",
       " 'Containing misinformation spreading in temporal social networks',\n",
       " 'Current Flow in Random Resistor Networks: The Role of Percolation in Weak and Strong Disorder',\n",
       " 'Demographic and Structural Characteristics to Rationalize Link Formation in Online Social Networks',\n",
       " 'Dense network motifs enhance dynamical stability',\n",
       " 'Design of endurable networks in the presence of aging',\n",
       " 'Discrimination universally determines reconstruction of multiplex networks',\n",
       " 'Dynamic networks and directed percolation',\n",
       " 'Effect of the Interconnected Network Structure on the Epidemic Threshold',\n",
       " 'Effects of mobility restrictions during COVID19 in Italy',\n",
       " 'Epidemic spreading and control strategies in spatial modular network',\n",
       " 'Eradicating Catastrophic Collapse in Interdependent Networks via Reinforced Nodes',\n",
       " 'Finding shortest and nearly shortest path nodes in large substantially incomplete networks',\n",
       " 'Fractal Boundaries of Complex Networks',\n",
       " 'Hidden Variables in Bipartite Networks',\n",
       " 'History-dependent percolation on multiplex networks',\n",
       " 'Hyperbolic Geometry of Complex Networks',\n",
       " 'Influence of autocorrelation on the topology of the climate network',\n",
       " 'Inter-similarity between coupled networks',\n",
       " 'Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition',\n",
       " 'Is Europe Evolving Toward an Integrated Research Area?',\n",
       " 'Jamming in complex networks with degree correlation',\n",
       " 'Latent geometry of bipartite networks',\n",
       " 'Link prediction with hyperbolic geometry',\n",
       " 'Local structure can identify and quantify influential global spreaders in large scale social networks',\n",
       " 'Mitigation of Malicious Attacks on Networks',\n",
       " 'NetSci High: Bringing Network Science Research to High Schools',\n",
       " 'Network Cosmology',\n",
       " 'Network Geometry',\n",
       " 'Network Physiology reveals relations between network topology and physiological function',\n",
       " 'Numerical evaluation of the upper critical dimension of percolation in scale-free networks',\n",
       " 'Optimal Paths in Complex Networks with Correlated Weights: The World-wide Airport Network',\n",
       " 'Optimal cost for strengthening or destroying a given network',\n",
       " 'Optimal resource allocation for network functionality',\n",
       " 'Optimal shattering of complex networks',\n",
       " 'Optimization of Network Robustness to Random Breakdowns',\n",
       " 'Origins of fractality in the growth of complex networks',\n",
       " 'Percolation in networks composed of connectivity and dependency links',\n",
       " 'Percolation of Hierarchical Networks and Networks of Networks',\n",
       " 'Percolation of Interdependent Networks with Inter-similarity',\n",
       " 'Percolation of a general network of networks',\n",
       " 'Percolation of localized attack on complex networks',\n",
       " 'Predictability of real temporal networks',\n",
       " 'Preferential Attachment in the Interaction between Dynamically Generated Interdependent Networks',\n",
       " 'Promoting information spreading by using contact memory',\n",
       " 'Propinquity drives the emergence of network structure and density',\n",
       " 'Resilience and efficiency in transportation networks',\n",
       " 'Revealing effective classifiers through network comparison',\n",
       " 'Robustness of Network of Networks with Interdependent and Interconnected links',\n",
       " 'Robustness of a Network Formed of Spatially Embedded Networks',\n",
       " 'Robustness of a Network of Networks',\n",
       " 'Robustness of interdependent networks under targeted attack',\n",
       " 'Robustness of networks with topologies of dependency links',\n",
       " 'Robustness of partially interdependent network formed of clustered networks',\n",
       " 'Role of bridge nodes in epidemic spreading: Different regimes and crossovers',\n",
       " 'Scale-free Resilience of Real Traffic Jams',\n",
       " 'Scaling for the Coalescence of Microfractures before Breakdown',\n",
       " 'Scaling theory of transport in complex networks',\n",
       " 'Self-similarity of complex networks',\n",
       " 'Stability of Climate Networks with Time',\n",
       " 'Statistical physics approaches to the complex Earth system',\n",
       " 'Strain engineering of ion migration in LiCoO2',\n",
       " 'Structural properties of spatially embedded networks',\n",
       " 'Structure of Business Firm Networks and Scale-Free Models',\n",
       " 'The Rise of China in the International Trade Network: A Community Core Detection Approach',\n",
       " 'The cost of attack in competing networks',\n",
       " 'The critical effect of dependency groups on the function of networks',\n",
       " 'The critical role of fresh teams in creating original and multi-disciplinary research',\n",
       " 'Topological properties and organizing principles of semantic networks']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2b37f91e",
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   "outputs": [
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     "text": [
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      "Node Count: 31\n"
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_check = community_context_dict[communities[4]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c4528ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Active learning based generative design for the discovery of wide bandgap materials',\n",
       " 'AlphaCrystal: Contact map based crystal structure prediction using deep learning',\n",
       " 'Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials',\n",
       " 'Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm',\n",
       " 'Discovery of 2D materials using Transformer Network based Generative Design',\n",
       " 'Generative Design of inorganic compounds using deep diffusion language models',\n",
       " 'Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction',\n",
       " 'High-throughput discovery of novel cubic crystal materials using deep generative neural networks',\n",
       " 'Inverse Design of Composite Metal Oxide Optical Materials based on Deep Transfer Learning',\n",
       " 'Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning',\n",
       " 'MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning',\n",
       " 'Materials Property Prediction with Uncertainty Quantification: A Benchmark Study',\n",
       " 'Physics guided deep learning generative models for crystal materials discovery',\n",
       " 'Piezoelectric modulus prediction using machine learning and graph neural networks',\n",
       " 'Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks',\n",
       " 'Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks',\n",
       " 'Probabilistic Generative Transformer Language models for Generative Design of Molecules',\n",
       " 'Scalable deeper graph neural networks for high-performance materials property prediction',\n",
       " 'Semi-supervised teacher-student deep neural network for materials discovery']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
   ]
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     "execution_count": 33,
     "metadata": {},
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   ],
   "source": [
    "G_check = community_context_dict[communities[5]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "202cd434",
   "metadata": {},
   "outputs": [
    {
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       "['A Network Science Summer Course for High School Students',\n",
       " 'A practical guide to methodological considerations in the controllability of structural brain networks',\n",
       " 'A statistical mechanics description of environmental variability in metabolic networks',\n",
       " 'Are Social Networks Really Balanced?',\n",
       " 'Brain Network Adaptability Across Task States',\n",
       " 'Brain Network Architecture: Implications for Human Learning',\n",
       " 'Centrality-Friendship Paradoxes: When Our Friends Are More Important Than Us',\n",
       " 'Communicability Angles Reveal Critical Edges for Network Consensus Dynamics',\n",
       " 'Communicability Graph and Community Structures in Complex Networks',\n",
       " 'Communicability in complex brain networks',\n",
       " 'Communicability in complex networks',\n",
       " 'Complex Networks as Hypergraphs',\n",
       " 'Compressibility of complex networks',\n",
       " 'Conformational Control of Mechanical Networks',\n",
       " 'Connecting the Dots: Identifying Network Structure via Graph Signal Processing',\n",
       " 'Convolutional Neural Networks Via Node-Varying Graph Filters',\n",
       " 'Cross-Linked Structure of Network Evolution',\n",
       " 'Data-Driven Control of Complex Networks',\n",
       " 'Distributed Inertial Best-Response Dynamics',\n",
       " 'Dynamic Network Centrality Summarizes Learning in the Human Brain',\n",
       " 'Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity',\n",
       " 'Evolution of network architecture in a granular material under compression',\n",
       " 'Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation',\n",
       " 'Functional Control of Oscillator Networks',\n",
       " 'Functional brain network architecture supporting the learning of social networks in humans',\n",
       " 'Generative Models for Network Neuroscience: Prospects and Promise',\n",
       " 'Grain fragmentation in sheared granular flow: weakening effects, energy dissipation, and strain localization',\n",
       " 'Graph Frequency Analysis of Brain Signals',\n",
       " 'Graph and Network Theory in Physics',\n",
       " 'How We Learn About our Networked World',\n",
       " 'Human Learning of Hierarchical Graphs',\n",
       " 'Human information processing in complex networks',\n",
       " 'Individual Differences in Learning Social and Non-Social Network Structures',\n",
       " 'Knowledge gaps in the early growth of semantic networks',\n",
       " 'Machine Learning Analysis of Complex Networks in Hyperspherical Space',\n",
       " 'Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation',\n",
       " 'Modularity maximisation for graphons',\n",
       " 'Network Analyses and Nervous System Disorders',\n",
       " 'Network Analysis of Particles and Grains',\n",
       " 'Network Comparison: Embeddings and Interiors',\n",
       " 'Network Topology Identification from Spectral Templates',\n",
       " 'Network architecture of energy landscapes in mesoscopic quantum systems',\n",
       " 'Network bypasses sustain complexity',\n",
       " 'Network constraints on learnability of probabilistic motor sequences',\n",
       " 'Network models in neuroscience',\n",
       " 'Network neuroscience for optimizing brain-computer interfaces',\n",
       " 'Network structure and dynamics of effective models of non-equilibrium quantum transport',\n",
       " 'Node and Edge Nonlinear Eigenvector Centrality for Hypergraphs',\n",
       " 'On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists',\n",
       " 'Path-dependent Dynamics Induced by Rewiring Networks of Inertial Oscillators',\n",
       " 'Persistent Homology Lower Bounds on High Order Network Distances',\n",
       " 'Predicting triadic closure in networks using communicability distance functions',\n",
       " 'Process reveals structure: How a network is traversed mediates expectations about its architecture',\n",
       " 'Resolving structural variability in network models and the brain',\n",
       " 'Small-World Propensity in Weighted, Real-World Networks',\n",
       " 'Spectral Measures of Bipartivity in Complex Networks',\n",
       " 'Spectral Scaling in Complex Networks',\n",
       " 'Stability of spontaneous, correlated activity in mouse auditory cortex',\n",
       " 'Statistical-mechanical approach to subgraph centrality in complex networks',\n",
       " 'Structural Pathways Supporting Swift Acquisition of New Visuo-Motor Skills',\n",
       " 'The emergent integrated network structure of scientific research',\n",
       " 'The growth and form of knowledge networks by kinesthetic curiosity',\n",
       " 'The landscape of NeuroImage-ing research',\n",
       " 'The network structure of scientific revolutions',\n",
       " 'The why, how, and when of representations for complex systems',\n",
       " 'Topological and geometric measurements of force chain structure',\n",
       " 'Topological data analysis of truncated contagion maps',\n",
       " 'Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment',\n",
       " \"Two's company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data\"]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
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  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1dc8c070",
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   "outputs": [
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      "Density: 0.275609756097561\n",
      "Node Count: 41\n"
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   "source": [
    "G_check = community_context_dict[communities[6]]['G']\n",
    "draw_community(G_check)"
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  {
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   "execution_count": 36,
   "id": "97e20914",
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    {
     "data": {
      "text/plain": [
       "['A New Transferable Interatomic Potential for Molecular Dynamics Simulations of Borosilicate Glasses',\n",
       " 'Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors',\n",
       " 'CONet: A Cognitive Ocean Network',\n",
       " 'Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance',\n",
       " 'Cooling-Rate Effects in Sodium Silicate Glasses: Bridging the Gap between Molecular Dynamics Simulations and Experiments',\n",
       " 'Deep Learning Aided Rational Design of Oxide Glasses',\n",
       " 'Designing optical glasses by machine learning coupled with a genetic algorithm',\n",
       " 'DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles',\n",
       " 'Direct Experimental Evidence for Differing Reactivity Alterations of Minerals following Irradiation: The Case of Calcite and Quartz',\n",
       " 'Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks',\n",
       " 'Does a dissolution-precipitation mechanism explain concrete creep in moist environments?',\n",
       " 'Dynamic Spectrum Leasing with Two Sellers',\n",
       " 'EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations',\n",
       " 'Fracture Toughness of Silicate Glasses: Insights from Molecular Dynamics Simulations',\n",
       " 'Glass Hardness: Predicting Composition and Load Effects via Symbolic Reasoning-Informed Machine Learning',\n",
       " 'GlassNet: a multitask deep neural network for predicting many glass properties',\n",
       " 'Ion-Exchange Strengthening of Glasses: Atomic Topology Matters',\n",
       " 'Is Cement a Glassy Material?',\n",
       " 'Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias',\n",
       " 'Machine Learning Forcefield for Silicate Glasses',\n",
       " 'Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures',\n",
       " 'Nano-Ductility in Silicate Glasses is Driven by Topological Heterogeneity',\n",
       " 'Nanoindentation-induced evolution of atomic-level properties in silicate glass: Insights from molecular dynamics simulations',\n",
       " 'Nature of Radiation-Induced Defects in Quartz',\n",
       " 'On the Allowable or Forbidden Nature of Vapor-Deposited Glasses',\n",
       " 'Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors',\n",
       " \"Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning\",\n",
       " 'Predicting the dissolution kinetics of silicate glasses using machine learning',\n",
       " \"Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning\",\n",
       " 'Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data',\n",
       " 'Revealing the Compositional Control of Electrical, Mechanical, Optical, and Physical Properties of Inorganic Glasses',\n",
       " 'Stretched Exponential Relaxation of Glasses at Low Temperature',\n",
       " 'Stretched Exponential Relaxation of Glasses: Origin of the Mixed Alkali Effect',\n",
       " 'Structural Dependence of Chemical Durability in Modified Aluminoborate Glasses',\n",
       " 'Structural, vibrational and thermal properties of densified silicates : insights from Molecular Dynamics',\n",
       " 'Structure and dynamics of liquid AsSe4 from ab initio molecular dynamics simulation',\n",
       " 'ToolNet: Using Commonsense Generalization for Predicting Tool Use for Robot Plan Synthesis',\n",
       " 'Topological Constraint Theory and Rigidity of Glasses',\n",
       " 'Topological Constraints and Rigidity of Network Glasses from Molecular Dynamics Simulations',\n",
       " \"Topological Control on Atomic Networks' Relaxation Under Stress\",\n",
       " 'Topological Control on Silicates Dissolution Kinetics',\n",
       " 'Topological Origins of Flexibility and Internal Stress in Sodium Aluminosilicate Glasses',\n",
       " 'Viscosity of Silica and Doped Silica Melts: Evidence for a Crossover Temperature']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "show_author_papers(df, G_check)"
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  {
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   "execution_count": 37,
   "id": "d9f6d485",
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   "outputs": [
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     "text": [
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      "Node Count: 22\n"
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   "source": [
    "G_check = community_context_dict[communities[7]]['G']\n",
    "draw_community(G_check)"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b1ef366",
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       "['A Multilayer Approach to Multiplexity and Link Prediction in Online Geo-Social Networks',\n",
       " 'A place-focused model for social networks in cities',\n",
       " 'A topological criterion for filtering information in complex brain networks',\n",
       " 'Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges',\n",
       " 'Applications of Temporal Graph Metrics to Real-World Networks',\n",
       " 'Centralities of Nodes and Influences of Layers in Large Multiplex Networks',\n",
       " 'Characteristic exponents of complex networks',\n",
       " 'Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks',\n",
       " 'Cultural Investment and Urban Socio-Economic Development: A Geo-Social Network Approach',\n",
       " 'Determinants of public cooperation in multiplex networks',\n",
       " 'Disentangling shock diffusion on complex networks: Identification through graph planarity',\n",
       " 'Economic Small-World Behavior in Weighted Networks',\n",
       " 'Efficiency of Scale-Free Networks: Error and Attack Tolerance',\n",
       " 'Emergence of multiplex communities in collaboration networks',\n",
       " 'Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models',\n",
       " 'Evolutionary dynamics of higher-order interactions in social networks',\n",
       " 'Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics',\n",
       " 'Explosive cooperation in social dilemmas on higher-order networks',\n",
       " 'Extracting Information from Multiplex Networks',\n",
       " 'Functional Multiplex PageRank',\n",
       " 'Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement',\n",
       " 'Homophily and missing links in citation networks',\n",
       " 'Hyper-diffusion on multiplex networks',\n",
       " 'Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web',\n",
       " 'Measuring and modelling correlations in multiplex networks',\n",
       " 'Mesoscopic Structures Reveal the Network Between the Layers of Multiplex Datasets',\n",
       " 'Model of spatial competition on discrete markets',\n",
       " 'Multilayer motif analysis of brain networks',\n",
       " 'Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods',\n",
       " 'Sensing and Modeling Human Behavior Using Social Media and Mobile Data',\n",
       " 'Sequential visibility-graph motifs',\n",
       " 'Social cohesion, structural holes, and a tale of two measures',\n",
       " 'Solving Graph-based Public Good Games with Tree Search and Imitation Learning',\n",
       " 'The Call of the Crowd: Event Participation in Location-based Social Services',\n",
       " 'The International Postal Network and Other Global Flows As Proxies for National Wellbeing',\n",
       " 'The advantages of interdisciplinarity in modern science',\n",
       " 'The multiplex dependency structure of financial markets',\n",
       " 'The new challenges of multiplex networks: measures and models',\n",
       " 'The shape of memory in temporal networks',\n",
       " 'Topological Properties and Temporal Dynamics of Place Networks in Urban Environments',\n",
       " 'Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks',\n",
       " 'Weighted Multiplex Networks']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
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   "cell_type": "code",
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    "G_check = community_context_dict[communities[8]]['G']\n",
    "draw_community(G_check)"
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   "execution_count": 40,
   "id": "e980d956",
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       "['#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election',\n",
       " 'A Large-Scale Community Structure Analysis In Facebook',\n",
       " 'A Parameterized Centrality Metric for Network Analysis',\n",
       " 'Botometer 101: Social bot practicum for computational social scientists',\n",
       " 'Capturing Edge Attributes via Network Embedding',\n",
       " 'Centrality Metric for Dynamic Networks',\n",
       " 'Clique Densification in Networks',\n",
       " 'Co-Contagion Diffusion on Multilayer Networks',\n",
       " 'Collaboration Drives Individual Productivity',\n",
       " 'Complex Systems of Secrecy: The Offshore Networks of Oligarchs',\n",
       " \"Computational Social Scientist Beware: Simpson's Paradox in Behavioral Data\",\n",
       " 'Contagion dynamics of extremist propaganda in social networks',\n",
       " 'Deep Neural Networks for Bot Detection',\n",
       " 'Defining and identifying Sleeping Beauties in science',\n",
       " 'Detecting criminal organizations in mobile phone networks',\n",
       " 'Emergence of Structural Inequalities in Scientific Citation Networks',\n",
       " 'Enhancing community detection using a network weighting strategy',\n",
       " 'Evolution of Online User Behavior During a Social Upheaval',\n",
       " 'Follow the Leader: Documents on the Leading Edge of Semantic Change Get More Citations',\n",
       " 'Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter',\n",
       " 'Graph Filters and the Z-Laplacian',\n",
       " 'How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events',\n",
       " 'Impact of Dynamic Interactions on Multi-Scale Analysis of Community Structure in Networks',\n",
       " 'In Situ Network and Application Performance Measurement on Android Devices and the Imperfections',\n",
       " 'Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks',\n",
       " 'Manipulation and abuse on social media',\n",
       " 'Mixing local and global information for community detection in large networks',\n",
       " 'Multi-Issue Bargaining With Deep Reinforcement Learning',\n",
       " 'Network Composition from Multi-layer Data',\n",
       " 'Network Structure and Resilience of Mafia Syndicates',\n",
       " 'Network Structure, Topology and Dynamics in Generalized Models of Synchronization',\n",
       " 'Non-Conservative Diffusion and its Application to Social Network Analysis',\n",
       " 'On Facebook, most ties are weak',\n",
       " 'Parameterized Centrality for Network Analysis',\n",
       " 'Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19',\n",
       " 'Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine',\n",
       " 'Quantifying the Effect of Sentiment on Information Diffusion in Social Media',\n",
       " 'Social Bots and Social Media Manipulation in 2020: The Year in Review',\n",
       " 'Structural Properties of Ego Networks',\n",
       " 'Structural and Cognitive Bottlenecks to Information Access in Social Networks',\n",
       " 'Structure of Heterogeneous Networks',\n",
       " 'The Gift That Keeps on Giving: Generosity is Contagious in Multiplayer Online Games',\n",
       " 'The Rise of Jihadist Propaganda on Social Networks',\n",
       " 'Topological Features of Online Social Networks',\n",
       " 'Towards Quantifying Sampling Bias in Network Inference',\n",
       " 'Twitter Spam and False Accounts Prevalence, Detection and Characterization: A Survey',\n",
       " 'User Effort and Network Structure Mediate Access to Information in Networks',\n",
       " 'Visualizing criminal networks reconstructed from mobile phone records']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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       "<text text-anchor=\"start\" x=\"709\" y=\"382\" font-size=\"10\">Hansong Nie</text><text text-anchor=\"start\" x=\"398\" y=\"376\" font-size=\"10\">Jiaying Liu</text><text text-anchor=\"start\" x=\"239\" y=\"132\" font-size=\"10\">Jie Hou</text><text text-anchor=\"start\" x=\"427\" y=\"492\" font-size=\"10\">Lianhua Chi</text><text text-anchor=\"start\" x=\"632\" y=\"270\" font-size=\"10\">Fuli Zhang</text><text text-anchor=\"start\" x=\"307\" y=\"110\" font-size=\"10\">Hanxiao Pan</text><text text-anchor=\"start\" x=\"169\" y=\"152\" font-size=\"10\">Teng Guo</text><text text-anchor=\"start\" x=\"432\" y=\"182\" font-size=\"10\">Xiaomei Bai</text><text text-anchor=\"start\" x=\"171\" y=\"20\" font-size=\"10\">Hongfei Lin</text><text text-anchor=\"start\" x=\"43\" y=\"89\" font-size=\"10\">Dongyu Zhang</text><text text-anchor=\"start\" x=\"90\" y=\"45\" font-size=\"10\">Zhitao Feng</text><text text-anchor=\"start\" x=\"88\" y=\"399\" font-size=\"10\">Ahsan Shehzad</text><text text-anchor=\"start\" x=\"399\" y=\"279\" font-size=\"10\">Xiangjie Kong</text><text text-anchor=\"start\" x=\"267\" y=\"23\" font-size=\"10\">Liang Yang</text><text text-anchor=\"start\" x=\"43\" y=\"304\" font-size=\"10\">Xiangtai Chen</text><text text-anchor=\"start\" x=\"586\" y=\"316\" font-size=\"10\">Hui Liu</text><text text-anchor=\"start\" x=\"325\" y=\"488\" font-size=\"10\">Wenqing Zheng</text><text text-anchor=\"start\" x=\"329\" y=\"282\" font-size=\"10\">Feng Xia</text><text text-anchor=\"start\" x=\"726\" y=\"244\" font-size=\"10\">Zafer AL-Makhadmeh</text><text text-anchor=\"start\" x=\"315\" y=\"328\" font-size=\"10\">Ivan Lee</text><text text-anchor=\"start\" x=\"119\" y=\"484\" font-size=\"10\">Yin Peng</text><text text-anchor=\"start\" x=\"613\" y=\"428\" font-size=\"10\">Yonghao Fu</text><text text-anchor=\"start\" x=\"664\" y=\"195\" font-size=\"10\">Amr Tolba</text><text text-anchor=\"start\" x=\"129\" y=\"324\" font-size=\"10\">Nargiz Sultanova</text><text text-anchor=\"start\" x=\"648\" y=\"512\" font-size=\"10\">Haoran Wei</text><text text-anchor=\"start\" x=\"581\" y=\"168\" font-size=\"10\">Tao Tang</text><text text-anchor=\"start\" x=\"231\" y=\"520\" font-size=\"10\">Ke Hou</text><text text-anchor=\"start\" x=\"26\" y=\"361\" font-size=\"10\">Mingliang Hou</text><text text-anchor=\"start\" x=\"212\" y=\"407\" font-size=\"10\">Jing Ren</text></svg>"
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      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_check = community_context_dict[communities[9]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ea542ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['An Energy-Efficient Localization Strategy for Smartphones',\n",
       " 'An Overview on Evaluating and Predicting Scholarly Article Impact',\n",
       " 'Big Networks: A Survey',\n",
       " 'CHIEF: Clustering with Higher-order Motifs in Big Networks',\n",
       " 'DINE: A Framework for Deep Incomplete Network Embedding',\n",
       " 'Data-driven Computational Social Science: A Survey',\n",
       " 'Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks',\n",
       " 'Graph Learning: A Survey',\n",
       " 'How to optimize an academic team when the outlier member is leaving?',\n",
       " 'Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks',\n",
       " 'MODEL: Motif-based Deep Feature Learning for Link Prediction',\n",
       " 'Matching Algorithms: Fundamentals, Applications and Challenges',\n",
       " 'Multivariate Relations Aggregation Learning in Social Networks',\n",
       " 'Network Representation Learning: From Traditional Feature Learning to Deep Learning',\n",
       " 'Neural Feedback Scheduling of Real-Time Control Tasks',\n",
       " 'On Content-centric Wireless Delivery Networks',\n",
       " 'Prediction Methods and Applications in the Science of Science: A Survey',\n",
       " 'Quantifying Success in Science: An Overview',\n",
       " 'R-CA: A Routing-based Dynamic Channel Assignment Algorithm for Wireless Mesh Networks',\n",
       " 'Random Walks: A Review of Algorithms and Applications',\n",
       " 'Socially-Aware Networking: A Survey',\n",
       " \"The dominance of big teams in China's scientific output\",\n",
       " 'Turing Number: How Far Are You to A. M. Turing Award?',\n",
       " 'Understanding the Advisor-advisee Relationship via Scholarly Data Analysis',\n",
       " 'User Popularity-based Packet Scheduling for Congestion Control in Ad-hoc Social Networks',\n",
       " 'Web of Scholars: A Scholar Knowledge Graph',\n",
       " 'Wireless Network Coding via Modified 802.11 MAC/PHY: Design and Implementation on SDR']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb3af479",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "53151e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seems to be that...\n",
    "# more dense: collaborative papers but fewer\n",
    "# less dense: more single author papers causing network sparcity\n",
    "\n",
    "# 'Online-updated High-order Collaborative Networks for Single Image Deraining',\n",
    "# found a paper about collaborative papers while studying author collaboration :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "389ab02e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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