https://github.com/sbonner0/GraphFingerprintComparison

A repository for the graph finger print comparison code, written in python and graph-tool.
https://github.com/sbonner0/GraphFingerprintComparison

Last synced: 3 months ago
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A repository for the graph finger print comparison code, written in python and graph-tool.

• Host: GitHub
• URL: https://github.com/sbonner0/GraphFingerprintComparison
• Owner: sbonner0
• License: gpl-3.0
• Created: 2016-05-15T18:28:03.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-10-12T21:13:08.000Z (over 3 years ago)
• Last Synced: 2024-01-17T21:27:07.043Z (6 months ago)
• Language: Python
• Size: 23.4 KB
• Stars: 7
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: license.txt

Lists

• awesome-graph-classification - [python Reference
• benedekrozemberczki-awesome-graph-classification - [python Reference

README

        
# Graph Fingerprint Comparison Code -

A repository for the graph finger print comparison code, written in Python using the Graph-Tool pacakge. The paper entiteld Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ which this is the code for can be found here - http://www.mlgworkshop.org/2016/

This code can be used to compare two graph based upon their fingerprint.

## Requirements

This code has been tested on Python 2.7.5+ and requires the following packages to function correctly:

* numpy 

* scipy

* graph-tool

## Usage

To replicate the results found in the paper, please run the *EXP.py* scripts. Custom graphs can be compared by editing the *GFP.py* with the location of any two graphs which you would like to be compared.

## Cite

Please cite the associated papers for this work if you use this code:

```

@inproceedings{bonner2016efficient,

  title={Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’},

  author={Bonner, Stephen and Brennan, John and Kureshi, Ibad and Stephen, McGough and Theodoropoulos, Georgios},

  booktitle={SIGKKD 12th International Workshop on Mining and Learning with Graphs (MLG)},

  year={2016}

}

```