SynC introduces a shared-weight TIGAE-based framework that alternates embedding learning and structure augmentation for graph clustering, with added fine-tuning for low-homophily graphs and reported gains on benchmarks.
Algorithm as 136: A k-means clustering algorithm
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$\texttt{SynC}$: Synergistic Boosting of Structure and Representation for Deep Graph Clustering
SynC introduces a shared-weight TIGAE-based framework that alternates embedding learning and structure augmentation for graph clustering, with added fine-tuning for low-homophily graphs and reported gains on benchmarks.