📝 Projects and Publications

PyDGC
Benyu Wu CODE

• PyDGC is a unified, flexible, and extensible framework for deep graph clustering.
• It provides a variety of graph clustering methods and datasets, making it easy to reproduce, implement, and evaluate algorithms.
• It also includes a comprehensive set of evaluation metrics for graph clustering tasks.

Synergistic Deep Graph Clustering Network
Benyu Wu, Shifei Ding*, Xiao Xu, Lili Guo, Ling Ding and Xindong Wu

CODE

• TIGAE uses simple linear transformation to introduce explicit graph Laplacian information to alleviate representation collapse.
• A synergistic framework of representation learning and structure augmentation is proposed to exploit the reciprocal relationship between them to jointly improve the embedding quality.
• Structure fine-tuning strategy improve the generalization ability of the model.

Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder
Shifei Ding, Benyu Wu, Ling Ding, Xiao Xu, Lili Guo, Hongmei Liao* and Xindong Wu

CODE

• EGAE optimizes the GAE from the perspectives of data dimension and graph convolution efficiency.
• Dynamic graph weight updating strategy adjust the structure during the training process.

Graph Clustering Network with Structure Embedding Enhanced
Shifei Ding, Benyu Wu, Xiao Xu, Lili Guo and Ling Ding*

CODE

• GC-SEE enhances the structural information in embeddings for clustering by utilizing different types of structural information.