NumGraph

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Num(py)Graph is a library for synthetic graph generation. The main principle of NumGraph is to be a lightweight library (i.e., numpy is the only dependency) that generates graphs from a broad range of distributions. Indeed, It implements several graph distributions in both the static and temporal domain.

Implemented distributions

Static Graphs

• Star graph
• Clique
• Two-dimensional rectangular grid lattice graph
• Random Tree
• Erdos Renyi
• Barabasi Albert
• Stochastic Block Model

Temporal Graphs

• Susceptible-Infected Dissemination Process Simulation
• Heat diffusion over a graph (closed form solution)
• Generic Euler's method approximation of a diffusion process over a graph

Installation

python3 -m pip install numgraph

Usage

>>> from numgraph import star_coo, star_full
>>> coo_matrix, coo_weights = star_coo(num_nodes=5, weighted=True)
>>> print(coo_matrix)
array([[0, 1],
       [0, 2],
       [0, 3],
       [0, 4],
       [1, 0],
       [2, 0],
       [3, 0],
       [4, 0]]

>>> print(coo_weights)
array([[0.89292422],
       [0.3743427 ],
       [0.32810002],
       [0.97663266],
       [0.74940571],
       [0.89292422],
       [0.3743427 ],
       [0.32810002],
       [0.97663266],
       [0.74940571]])

>>> adj_matrix = star_full(num_nodes=5, weighted=True)
>>> print(adj_matrix)
array([[0.        , 0.72912008, 0.33964166, 0.30968042, 0.08774328],
       [0.72912008, 0.        , 0.        , 0.        , 0.        ],
       [0.33964166, 0.        , 0.        , 0.        , 0.        ],
       [0.30968042, 0.        , 0.        , 0.        , 0.        ],
       [0.08774328, 0.        , 0.        , 0.        , 0.        ]])

Other examples can be found in test/plot_static.py and test/plot_temporal.py.