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README.md

GraphSTONE

A TensorFlow implementation of GraphSTONE, as described in our paper:

Graph Structural-topic Neural Network (KDD 2020, Research Track)

See our Paper, and Slides.

How to Use

python main.py

Dependencies

Tensorflow 1.10.0, Networkx 1.11, Python 3

Data

We provide cora and ppi datasets as examples under data/cora and data/ppi.

Note that the data/dataset_name/features.npy has undergone a dimensionality reduction via PCA, and is not identical to the original cora features.

Parameters

For parameter settings, please see conf.json.

Some parameter definitions:

Name Default Note
dataset cora dataset name
input_node_feature True input original node features ("True") or not ("False")
PreProcess/number_paths 50 number of paths from a center node, for generating "word" and "document" concepts on graphs
PreProcess/path_length 15 max length of random walks from a center node, for generating "word" and "document" concepts on graphs
TopicModel/number_topic 5 number of structural-topics
TopicModel/max_features_dim 2500 max topic_features (for the input of structural-topic GNN) dimension
TopicGCN/max_training_steps 5000 max steps for training

Cite

@inproceedings{long2020graph,
  title={Graph Structural-topic Neural Network},
  author={Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1065--1073},
  year={2020}
}

Acknowledgments

Certain parts of this project are partially derived from GraLSP and AnchorRecovery.

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An implementation of "Graph Structural-topic Neural Network"

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