Uncovering Capabilities of Model Pruning in Graph Contrastive Learning
J Wu, X Chen, S Li - Proceedings of the 32nd ACM International …, 2024 - dl.acm.org
Graph contrastive learning has achieved great success in pre-training graph neural
networks without ground-truth labels. Leading graph contrastive learning follows the …
networks without ground-truth labels. Leading graph contrastive learning follows the …
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
Real-world graphs have inherently complex and diverse topological patterns, known as
topological heterogeneity. Most existing works learn graph representation in a single …
topological heterogeneity. Most existing works learn graph representation in a single …