3 citations · 3 across the 16 of their papers we have counts for
5 papers · 1 filter
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Yunfei Liu, Jintang Li, Yuehe Chen +9
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning…
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Yuchang Zhu, Jintang Li, Yatao Bian +2
Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sens…
State Space Models on Temporal Graphs: A First-Principles Study
Jintang Li, Ruofan Wu, Xinzhou Jin +3
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors.…
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
Yuchang Zhu, Jintang Li, Zibin Zheng +1
Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and…
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
Huizhe Zhang, Jintang Li, Liang Chen +1
Graph Transformers (GTs) with powerful representation learning ability make a huge success in wide range of graph tasks. However, the costs behind outstanding performances of GTs a…