22 citations · 53 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Cyclic Label Propagation for Graph Semi-supervised Learning
Zhao Li, Yixin Liu, Zhen Zhang +3
Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers…
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
Ning Ma, Jiajun Bu, Jieyu Yang +5
Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Althoug…