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Implementation and application of graph theory, social network mining, reinforcement learning, and inverse reinforcement learning.

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Qiong-Hu/Large-Scale_Social_and_Complex_Networks

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Large-Scale Social and Complex Networks

Content

  • Generate large random networks using Erdős–Rényi (ER) model and Preferential Attachment (PA) model.
  • Analyze community structure of large networks, including Giant Connected Component (GCC) size and modularity.
  • Modify preferential attachment model to penalize the age of a node in large random graph.
  • Implemente random walk on large random network to simulate Google’s PageRank algorithm.

Content

  • Explore structural properties of undirected social network (Facebook dataset) using connectivity and degree distribution.
  • Explore structural properties of personalized network of core nodes using Fast-Greeedy, Edge-Betweenness, and Infomap community detection algorithms.
  • Explore characteristics of nodes in the personalized networks using Embeddedness and Dispersion.
  • Implement friend recommendation in personalized networks using neighborhood-based measures, including common neighbor measure, Jaccard measure, and Adamic-Adar measure, and evaluate with average accuracy measure.
  • Explore the community structure of directed social network (Google+ dataset), defined by homogeneity and completeness.

Content

  • Model the environment of the agent by Markov Decision Process (MDP).
  • Implement value iteration algorithm of Reinforcement Learning (RL).
  • Implement the IRL algorithm using Linear Programming (LP) formulation.
  • Evaluate the performance of IRL algorithm by comparing the extracted reward function and optimal policy map with ground truth.

Content

  • Construct a correlation graph using correlation coefficient computed among stock-return time series data.
  • Extract the Minimum Spanning Tree (MST) of the correlation graph and interpret it.
  • Predict the market sector of an unknown stock, and evaluate the performance using sector clustering in MST.
  • Simulate the influence of traffic block on the flow between certain places, and estimate how many roads could be blocked before paralyzing the traffic in Los Angeles area.