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https://github.com/caesar0301/graphsim
Graph similarity algorithms based on NetworkX.
https://github.com/caesar0301/graphsim
graph graph-similarity-algorithms networkx numpy python scientific tacsim
Last synced: 10 days ago
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Graph similarity algorithms based on NetworkX.
- Host: GitHub
- URL: https://github.com/caesar0301/graphsim
- Owner: caesar0301
- License: bsd-3-clause
- Created: 2015-11-04T14:55:02.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2019-08-27T11:21:57.000Z (about 5 years ago)
- Last Synced: 2024-10-16T05:31:12.343Z (25 days ago)
- Topics: graph, graph-similarity-algorithms, networkx, numpy, python, scientific, tacsim
- Language: Python
- Size: 56.6 KB
- Stars: 167
- Watchers: 8
- Forks: 25
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
graphsim
--------
Graph similarity algorithms based on NetworkX.
**BSD Licensed**
[](https://travis-ci.org/caesar0301/graphsim)
[](https://pypi.python.org/pypi/graphsim)
[](https://pypi.python.org/pypi/graphsim)
[](https://pypi.python.org/pypi/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 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:
```bash
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:
```bash
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](https://ieeexplore.ieee.org/document/6785743)]
* `gs.nsim_bvd04`: node-node similarity matrix, by Blondel et al. [[paper](https://dl.acm.org/citation.cfm?id=1035557)]
* `gs.hits`: the hub and authority scores for nodes, by Kleinberg. [[paper](https://dl.acm.org/citation.cfm?id=324140)]
* `gs.nsim_hs03`: node-node similarity with mismatch penalty, by Heymans et al. [[paper](https://www.ncbi.nlm.nih.gov/pubmed/12855450)]
* `gs.simrank`: A Measure of Structural-Context Similarity, by Jeh et al. [[paper](https://dl.acm.org/citation.cfm?id=775126)]
* `gs.simrank_bipartite`: SimRank for bipartite graphs, by Jeh et al. [[paper](https://dl.acm.org/citation.cfm?id=775126)]
* `gs.tacsim`: Topology-Attributes Coupling Similarity, by Chen et al. [[paper](http://dx.doi.org/10.1016/j.pmcj.2017.02.001)]
* `gs.tacsim_combined`: A combined topology-attributes coupling similarity, by Chen et al. [[paper](http://dx.doi.org/10.1016/j.pmcj.2017.02.001)]
* `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