Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

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 <>

Release History

This version
History Node


History Node


History Node


History Node


History Node

History Node

History Node

History Node

History Node

History Node

History Node

History Node

History Node


History Node

History Node

History Node


History Node


History Node


Download Files

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

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(16.8 kB) Copy SHA256 Hash SHA256
Source None Dec 23, 2017

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers DreamHost DreamHost Log Hosting