15 citations · 18 across the 6 of their papers we have counts for
6 papers
PyGDA: A Python Library for Graph Domain Adaptation
Zhen Zhang, Meihan Liu, Bingsheng He
Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their…
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou +5
Graph Neural Networks (GNNs) have achieved great success but are often considered to be challenged by varying levels of homophily in graphs. Recent \textit{empirical} studies have…
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
Meihan Liu, Zhen Zhang, Jiachen Tang +3
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the p…
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…
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…
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…