Skip to main content

Graph similarity algorithms based on NetworkX.

Project description


Graph similarity algorithms based on NetworkX.

**BSD Licensed**

[![Build Status](](


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 C_INCLUDE_PATH=~/usr/include:$C_INCLUDE_PATH


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


>>> import graphsim as gs

Supported algorithms

* `gs.ascos`: Asymmetric network Structure COntext Similarity, by Hung-Hsuan Chen et al.
* `gs.nsim_bvd04`: node-node similarity matrix, by Blondel et al.
* `gs.hits`: the hub and authority scores for nodes, by Kleinberg.
* `gs.nsim_hs03`: node-node similarity with mismatch penalty, by Heymans et al.
* `gs.simrank`: A Measure of Structural-Context Similarity, by Jeh et al.
* `gs.simrank_bipartite`: SimRank for bipartite graphs, by Jeh et al.
* `gs.tacsim`: Topology-Attributes Coupling Similarity, by Xiaming Chen et al.
* `gs.tacsim_combined`: A combined topology-attributes coupling similarity, by Xiaming Chen et al.
* `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.


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 = "",
author = "Xiaming Chen and Haiyang Wang and Siwei Qiang and Yongkun Wang and Yaohui Jin"


Xiaming Chen <>

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

graphsim-0.2.12.tar.gz (16.8 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page