Implementation of NEST, ASONAM 2018.

Please cite the following work if you find the code useful.

@inproceedings{yang2018node,
	Author = {Yang, Carl and Liu, Mengxiong and Zheng, Vincent and Han, Jiawei},
	Booktitle = {ASONAM},
	Title = {Node, motif and subgraph: learning network functional blocks through structural convolution},
	Year = {2018}
}

Contact: Carl Yang ([email protected])

Dependencies

pip install dill tqdm tensorflow

Pipeline

• Match instances with motifs

# for cascade task
python preprocess.py
# for classification task
python prepare.py

• Training and evaluating

python main.py

Parameters

• To change dataset, modify the data_dir parameter in flags in main.py
• kernel.json under each dataset directory defines the motifs to be matched, modify it to customize the motifs
• For details of hyper-parameters, please refer to the comment in flags in main.py

Dataset

• graph.txt contains the edge list of the complete graph, graph is undirected
• train.txt contains the training data, each line is a data point, each data point is a subgraph
• train/subgraph/ contains all the data points, one data point per file, each represented as an edge list
• train/meta/ contains all the matched instances of motifs, one data point per file