3 citations · 3 across the 8 of their papers we have counts for
12 papers · 1 filter
Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
Huizhe Zhang, Yuchang Zhu, Huazhen Zhong +2
Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labe…
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4
Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such…
SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
Yuchang Zhu, Jintang Li, Huizhe Zhang +2
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. De…
Are Large Language Models In-Context Graph Learners?
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or…
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…