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

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Last update: Sep 8, 2022

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Computer Vision DualMessagePassing

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

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

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

Related tags

Overview

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

Introduction

Reproduction

Package Dependencies

Citation

Miscellaneous

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Comments

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