2 citations · 3 across the 16 of their papers we have counts for
4 papers · 1 filter
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning
Jiajun Zhu, Siqi Miao, Rex Ying +1
The interpretability of machine learning models has gained increasing attention, particularly in scientific domains where high precision and accountability are crucial. This resear…
Pairwise Alignment Improves Graph Domain Adaptation
Shikun Liu, Deyu Zou, Han Zhao +1
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model…
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
Yuhong Luo, Pan Li
Temporal graph representation learning (TGRL) is essential for modeling dynamic systems in real-world networks. However, traditional TGRL methods, despite their effectiveness, ofte…