This repository is the official implementation of 'AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching'.

Official Download: Here can download official paper.

Free Download: Here can download paper.

Official Download: Here can download official paper.

Free Download: Here can download paper.

• python3.7
• pytorch==1.9.0
• dgl==0.8.0
• networkx==2.6.2
• numpy==1.21.5
• matplotlib==3.4.2

This code repository is heavily built on DGL, which is a DEEP GRAPH LIBRARY for Graph Computation. Please refer here for how to install and utilize the library.

There are some samples in './data/'. You should generate data before training.

To generate the Synthetic Data, run this command:

python creatData.py

You can use dgraph.__getitem __() in dataSet.py to process one sample and then use collate() in dataSet.py to batch data.

You can use this command to see one data sample's interior structure.

from dgl.data.utils import save_graphs, get_download_dir, load_graphs

graph_pair_path = './data/COX2/train/0.bin'  ## one data sample's path
graph_pair, label_dict = load_graphs(graph_pair_path)
graph_data = graph_pair[0]  ##  one sample's data graph in DGL form
graph_query = graph_pair[1]  ##  one sample's query graph in DGL form
label = label_dict['glabel']  ##  Ground-Truth matching relatinship
print(graph_data, graph_query, label)

To train the model(s) in the paper, run this command:

python train.py

If you find our paper/code is useful, please consider citing our paper:

@article{lan2023aednet,
  title={AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching},
  author={Lan, Zixun and Ma, Ye and Yu, Limin and Yuan, Linglong and Ma, Fei},
  journal={Pattern Recognition},
  volume={133},
  pages={109033},
  year={2023},
  publisher={Elsevier}
}