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

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.

• tqdm
• numpy
• pandas
• scipy
• numba >= 0.54.0
• python-igraph == 0.9.11
• torch >= 1.7.0
• dgl >= 0.6.0

Please refer to SubgraphCountingMatching and UnsupervisedNodeClassification

@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}
}

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