$\texttt{SynC}$: Synergistic Boosting of Structure and Representation for Deep Graph Clustering

In three linesSynC, a deep graph clustering framework, leverages synergistic relationship between representation learning and structure augmentation via a Transform Input Graph Auto-Encoder (TIGAE). The model shares weights across two stages to reduce parameters and improves generalization on low homophily graphs.

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