4 papers
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
Wei Chen, Guo Ye, Yakun Wang +5
Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graph…
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
Yakun Wang, Daixin Wang, Hongrui Liu +4
Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive p…
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
Yakun Wang, Binbin Hu, Shuo Yang +6
The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a…
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Qian Zhao, Shuo Yang, Binbin Hu +5
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area. The WSDM Cup 2022 seeks for solutions that pre…