15 citations · 17 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 15 cited
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Zhen Zhang, Meihan Liu, Anhui Wang +4
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. Howeve…
cs.LG2024★ 1 cited
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Meihan Liu, Zeyu Fang, Zhen Zhang +4
Unsupervised Graph Domain Adaptation (UGDA) aims to transfer knowledge from a labelled source graph to an unlabelled target graph in order to address the distribution shifts betwee…
cs.LG2023★ 1 cited
Homophily-enhanced Structure Learning for Graph Clustering
Ming Gu, Gaoming Yang, Sheng Zhou +5
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existi…