19 citations · 24 across the 5 of their papers we have counts for
4 papers · 1 filter
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
Zhichen Zeng, Boxin Du, Si Zhang +3
Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning…
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
Shuaicheng Zhang, Haohui Wang, Si Zhang +1
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect…
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
Haohui Wang, Yuzhen Mao, Yujun Yan +8
Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. Howeve…
Do We Really Need Complicated Model Architectures For Temporal Networks?
Weilin Cong, Si Zhang, Jian Kang +5
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we fo…