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.
Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
$\texttt{SynC}$: 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.