graphsim 0.2.12

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.
* `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>