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FUGAL: Feature-fortified Unrestricted Graph Alignment

This repository contains the reference implementation for FUGAL for the paper "FUGAL: Feature-fortified Unrestricted Graph Alignment".

Installation

  1. Install PyTorch, numpy, tqdm, networkx, sklearn, and scipy.

Run FUGAL

  1. Import helpers/pred.py which has the source implementation of FUGAL
  2. Call predict_alignment() function in helpers/pred.py which takes arguments (queries, targets, mu, niter)
  3. queries, targets should be a list of networkx graphs.
  4. Paramters mu, niter for benchmark datasets are provided in the paper.

Datasets

  1. Benchmark Networks:
    1. Graphs with Real Noise: MultiMagna, HighSchool, Voles
    2. Real Graphs: Arenas, inf-euroroad, ca-Netscience, bio-celegans, ACM-DBLP
    3. Synthetic Graphs: Newmann-Watts
  2. Real networks data is provided in data/ folder.
  3. Noise injected real-world graphs used in the experiments are provided as well.
  4. For generating Newmann-Watts graphs (or) to create noisy versions of the real graphs use the framework