Cliquematch

Latest version: v3.0.0

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3.5.0

[4]: https://github.com/ahgamut/cliquematch/blob/master/LICENSE
[5]: https://travis-ci.com/ahgamut/cliquematch
[6]: https://ci.appveyor.com/project/ahgamut/cliquematch
[wiki]: https://en.wikipedia.org/wiki/Clique_(graph_theory)#Definitions
[wheels]: https://pypi.org/project/cliquematch/
[finger]: https://patents.google.com/patent/US5933516A/en
[imreg]: https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/theiler2012.pdf
[molecule]: https://doi.org/10.1016/S1093-3263(97)00089-2

2.2.0

`cliquematch`
Finding correspondence via maximum cliques in large graphs

[![pyvers](https://img.shields.io/badge/python-3.5+-blue.svg)][3] [![license](https://img.shields.io/github/license/ahgamut/cliquematch)][4] [![travis build](https://travis-ci.com/ahgamut/cliquematch.svg?branch=master)][5] [![appveyor build](https://ci.appveyor.com/api/projects/status/27r2qy8mbog04bhg?svg=true)][6]

The `cliquematch` package aims to do two specific things:

1. Find [maximum cliques][wiki] in large sparse undirected graphs, as quickly and efficiently as possible.
`cliquematch` provides fast C++ implementations solving the maximum clique and clique enumeration problem.

2. Many applications of the clique problem, such as [fingerprint matching][finger], [image registration][imreg], or [molecule alignment][molecule] involve construction of a graph from two given datasets. `cliquematch` provides flexible methods to construct large sparse undirected graphs in-memory for such applications.

Source code at https://github.com/ahgamut/cliquematch
View the documentation at https://cliquematch.readthedocs.io/

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