SynC: Synergistic Boosting of Structure and Representation for Deep Graph Clustering

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks

Background:

• Deep graph clustering (DGC) using graph neural networks (GNNs) shows promise.
• Existing DGC methods fail to leverage the reciprocal relationship between representation learning and structure augmentation.
• GNN-based models exhibit poor generalization on graphs with low homophily.

Purpose of the Study:

• Propose a novel graph clustering framework, SynC, to address limitations in DGC.
• Enhance the synergistic relationship between representation learning and structure augmentation.
• Improve the generalization ability of GNNs on low homophily graphs.

Main Methods:

• Introduced a transform input graph autoencoder (TIGAE) to mitigate representation collapse and guide structure augmentation.
• Employed a two-stage approach with shared weights for synergistic boosting and parameter reduction.
• Incorporated a structure fine-tuning (SF) strategy to improve generalization on low homophily graphs.

Main Results:

• SynC achieves superior performance on benchmark datasets compared to existing methods.
• The proposed TIGAE effectively generates high-quality embeddings.
• The SF strategy enhances model generalization on challenging low homophily graphs.

Conclusions:

• SynC offers a synergistic approach to deep graph clustering, improving both representation learning and structure augmentation.
• The framework demonstrates significant advancements in handling graphs with low homophily.
• SynC provides a robust and efficient solution for various graph clustering tasks.