rinataynulin / py_graphs

Framework for clustering graphs using various measures

py_graphs

Framework for clustering graphs using various distances/proximity measures.

List of distances/proximity measures:

• Shortest Path and Commute Time (and linear combination)
• plain Walk (Von Neumann diffusion kernel)
• Forest (Regularized Laplacian kernel)
• Communicability (Exponential diffusion kernel)
• Heat kernel (Laplacian exponential diffusion kernel)
• logarithmic versions of Walk, Forest, Communicability, Heat
• Sigmoid Commute Time
• Sigmoid Corrected Commute Time
• Randomized Shortest Path
• Free Energy
• Normalized Heat
• Regularized Laplacian
• Personalized PageRank
• Modified Personalized PageRank
• Heat Personalized PageRank

List of clustering algoritms:

• Kernel k-means
• Spectral clustering
• Ward

List of graph generators:

• Stochastic Block Model

List of graph samples:

• Football
• Polbooks
• Polblogs
• Zachary
• Newsgroup

Publications

If you wish to use and cite this work, please cite this earlier paper which used many of the same concepts and methods (a newer publication is in preparation):

Ivashkin and Chebotarev, "Do logarithmic proximity measures outperform plain ones in graph clustering?." International Conference on Network Analysis, Springer, 2016.