Graph similarity algorithms based on NetworkX.
Project description
graphsim
--------
Graph similarity algorithms based on NetworkX.
**BSD Licensed**
[![Build Status](https://travis-ci.org/caesar0301/graphsim.svg?branch=master)](https://travis-ci.org/caesar0301/graphsim)
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 Issue:** `sudo` is required to give permission to install cpp modules into /usr/local/{lib,include}.
**NOTE**: `libtacsim` was tested on Ubuntu 12.04, Ubuntu 16.04, CentOS 6.5 and Mac OS 10.11.2.
Usage
-----
>>> 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.
Citation
----------
```tex
@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>
--------
Graph similarity algorithms based on NetworkX.
**BSD Licensed**
[![Build Status](https://travis-ci.org/caesar0301/graphsim.svg?branch=master)](https://travis-ci.org/caesar0301/graphsim)
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 Issue:** `sudo` is required to give permission to install cpp modules into /usr/local/{lib,include}.
**NOTE**: `libtacsim` was tested on Ubuntu 12.04, Ubuntu 16.04, CentOS 6.5 and Mac OS 10.11.2.
Usage
-----
>>> 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.
Citation
----------
```tex
@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>
Project details
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