DNMF

Unofficial Python implementation of the Discrete Non-negative Matrix Factorization (DNMF) overlapping community detection algorithm

Paper

Ye, Fanghua, Chuan Chen, Zibin Zheng, Rong-Hua Li, and Jeffrey Xu Yu. 2019. “Discrete Overlapping Community Detection with Pseudo Supervision.” In 2019 IEEE International Conference on Data Mining (ICDM), 708–17. https://doi.org/10.1109/ICDM.2019.00081.

Requirements

• python>=3.7.1
• torch>=1.9.1

Quick start

• To install the package run one of the two commands:

  • python -m pip install dnmf-python (installation from PyPI)
  • python setup.py install (compile from source, if cloned the repository)

• To run the algorithm, load the graph adjacency matrix into a torch.FloatTensor (for ex. A), then call:

  from dnmf.DNMF import DNMF
  dnmf = DNMF()
  F = dnmf(A)
  

• To run a quick test of the algorithm with an example graph, run python test.py from inside the src/dnmf/ directory

Config

The DNMF module supports the following hyperparameters as arguments:

• alpha: tradeoff parameter for the U-subproblem
• beta: tradeoff parameter for the F-subproblem
• gamma: regularization parameter
• k: desired number of overlapping communities
• num_outer_iter: number of iterations for the outer loop (SDP iterations)
• num_inner_iter: number of iterations for the inner loops (U and F subproblems)

Author

Andrej Janchevski

[email protected]

EPFL STI IEM LIONS