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graphsim

Graph similarity algorithms based on NetworkX.

BSD Licensed

Build Status PyPI PyPI PyPI

Install

First, install building tool:

$ yum install -y scons

On Mac OS:

$ brew install scons

Then install graphsim via PyPI:

$ pip install -U graphsim

Permission Issues

By default, sudo is required to give permission to install cpp modules into system /usr/local/{lib,include}.

If you prefer local installation, following instructions may help you:

export LIBTACSIM_LIB_DIR=~/usr/lib/
export LIBTACSIM_INC_DIR=~/usr/include/

pip install -U graphsim

Make sure that the local directories are aware for C linkers:

export LD_LIBRARY_PATH=~/usr/lib:$LD_LIBRARY_PATH
export C_INCLUDE_PATH=~/usr/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=~/usr/include:$CPLUS_INCLUDE_PATH

Coverage

NOTE: libtacsim was tested on Ubuntu 12.04, Ubuntu 16.04, CentOS 6.5 and Mac OS 10.11.2, 10.13.2.

Usage

>>> import graphsim as gs

Supported algorithms

  • gs.ascos: Asymmetric network Structure COntext Similarity, by Hung-Hsuan Chen et al. [paper]
  • gs.nsim_bvd04: node-node similarity matrix, by Blondel et al. [paper]
  • gs.hits: the hub and authority scores for nodes, by Kleinberg. [paper]
  • gs.nsim_hs03: node-node similarity with mismatch penalty, by Heymans et al. [paper]
  • gs.simrank: A Measure of Structural-Context Similarity, by Jeh et al. [paper]
  • gs.simrank_bipartite: SimRank for bipartite graphs, by Jeh et al. [paper]
  • gs.tacsim: Topology-Attributes Coupling Similarity, by Chen et al. [paper]
  • gs.tacsim_combined: A combined topology-attributes coupling similarity, by Chen et al. [paper]
  • gs.tacsim_in_C: an efficient implementation of TACSim in pure C.
  • gs.tacsim_combined_in_C: an efficient implementation of combined TACSim in pure C.

Supported utilities

  • gs.normalized: L2 normalization of vectors, matrices or arrays.
  • gs.node_edge_adjacency: Obtain node-edge adjacency matrices in source and dest directions.

Citation

@article{Chen2017,
  title = "Discovering and modeling meta-structures in human behavior from city-scale cellular data",
  journal = "Pervasive and Mobile Computing ",
  year = "2017",
  issn = "1574-1192",
  doi = "http://dx.doi.org/10.1016/j.pmcj.2017.02.001",
  author = "Xiaming Chen and Haiyang Wang and Siwei Qiang and Yongkun Wang and Yaohui Jin"
}

Author

Xiaming Chen chenxm35@gmail.com