Heat Kernel based Community Detection

Installation

Requirements

Compile

Related Code

• Fast Bidirectional Probability Estimation in Markov Models, NIPS 2015. [pdf] [code]
• Heat Kernel Based Community Detection, KDD 2014. [pdf] [code]
• Solving Local Linear Systems with Boundary Conditions Using Heat Kernel Pagerank, Internet Mathematics 2015. [pdf] [code]
• Parallel Local Graph Clustering, VLDB 2016. [pdf] [code]

How to Use

Third-party Library

• Mersenne Twist pseudorandom number generator: http://www.cs.hmc.edu/~geoff/mtwist.html

Dataset

• actor: http://konect.uni-koblenz.de/networks/actor-collaboration
• wordnet: http://konect.uni-koblenz.de/networks/wordnet-words
• flixster: http://konect.uni-koblenz.de/networks/flixster
• dblp, orkut, LJ, friendster: snap.stanford.edu/data/index.html
• twitter: http://an.kaist.ac.kr/traces/WWW2010.html
• flickr: http://konect.uni-koblenz.de/networks/flickrEdges
• facebook: http://konect.uni-koblenz.de/networks/facebook-wosn-links
• cit-Patents: https://graphchallenge.mit.edu/data-sets

How to use

Requirements

• GNU Linux system
• GCC 4.8
• Boost library

Compile

$ cmake .
$ make clena all -j4

Run

$ ./hk -a teaplus -f ./data/ -g dblp -n 50 -s seeds.txt

Citation

@inproceedings{yang2019efficient,
  title={Efficient Estimation of Heat Kernel PageRank for Local Clustering},
  author={Yang, Renchi and Xiao, Xiaokui and Wei, Zhewei and Bhowmick, Sourav S and Zhao, Jun and Li, Rong-Hua},
  booktitle={Proceedings of the 2019 International Conference on Management of Data},
  pages={1339--1356},
  year={2019}
}