HyperGC: Learning Hypergraph Representations via Full Hyperedge Reconstruction and Contrastive Evaluation
The proposed HyperGC is a hybrid self-supervised HRL framework that unifies generative and contrastive learning through two core strategies: (S1) full hyperedge reconstruction via progressive selection with dynamic target cross-entropy, and (S2) multi-view contrastive evaluation without handcrafted negative samples.