Source Code for AAAI 2022 paper "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching"

Overview

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching

This repository is an official implementation of the paper Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching.

Introduction

We propose dual message passing neural networks (DMPNNs) to enhance the substructure representation learning in an asynchronous way for subgraph isomorphism counting and matching as well as unsupervised node classification.

Reproduction

Package Dependencies

  • tqdm
  • numpy
  • pandas
  • scipy
  • numba >= 0.54.0
  • python-igraph
  • torch >= 1.7.0
  • dgl >= 0.6.0

Please refer to SubgraphCountingMatching and UnsupervisedNodeClassification

Citation

@inproceedings{liu2022graph,
  author    = {Xin Liu, Yangqiu Song},
  title     = {Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching},
  booktitle = {AAAI},
  year      = {2022}
}

Miscellaneous

Please send any questions about the code and/or the algorithm to [email protected].

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Comments
  • run evaluate.py have a problem

    run evaluate.py have a problem

    Traceback (most recent call last): File "/home/faker/Desktop/code/DualMessagePassing-main/SubgraphCountingMatching/evaluate.py", line 439, in eval_metric, eval_results = evaluate_epoch( File "/home/faker/Desktop/code/DualMessagePassing-main/SubgraphCountingMatching/evaluate.py", line 119, in evaluate_epoch pred_c, (pred_v, pred_e), ((p_v_mask, p_e_mask), (g_v_mask, g_e_mask)) = model(pattern, graph) ValueError: too many values to unpack (expected 3)

    opened by Faker0715 3
Owner
HKUST-KnowComp
Knowledge Computation Group@HKUST, led by Yangqiu Song
HKUST-KnowComp
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