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This repository contains a Python implementation of methods presented in the following paper:

Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management (CIKM), 2018.

Paper: https://gemslab.github.io/papers/heimann-2018-regal.pdf

Please consider citing this paper if you find the code helpful.

@inproceedings{DBLP:conf/cikm/HeimannSSK18,
  author    = {Mark Heimann and
               Haoming Shen and
               Tara Safavi and
               Danai Koutra},
  title     = {{REGAL:} Representation Learning-based Graph Alignment},
  booktitle = {Proceedings of the 27th {ACM} International Conference on Information
               and Knowledge Management, {CIKM} 2018, Torino, Italy, October 22-26,
               2018},
  pages     = {117--126},
  publisher = {{ACM}},
  year      = {2018},
}

Included is code for REGAL, our node embedding framework for network alignment, and its component node embedding method xNetMF.
This is only a reference implementation; without doubt it can be much improved, but we hope it is helpful!

DEPENDENCIES

numpy, scipy, networkx (all may be installed with pip) Tested with Python 3.8.5 and Python 2.7.16

EXAMPLE

INSTRUCTIONS - xNetMF embeddings only

INSTRUCTIONS - REGAL alignments from xNetMF embeddings