213 citations · 331 across the 17 of their papers we have counts for
3 papers · 1 filter
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes. Despite development…
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
Towards Faithful and Consistent Explanations for Graph Neural Networks
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critic…