6 papers
Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
Yunlong Chu, Minglai Shao, Zengyi Wo +4
Graph Neural Networks (GNNs) face a fundamental adaptability challenge: their fixed message-passing architectures struggle with the immense diversity of real-world graphs, where op…
Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment
Yuhang Liu, Minglai Shao, Zengyi Wo +5
Pre-training Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) is central to web-scale applications such as search, recommendation, and knowledge discovery. However,…
Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
Zengyi Wo, Wenjun Wang, Minglai Shao +3
In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous…
Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Zengyi Wo, Chang Liu, Yumeng Wang +2
Graph Neural Networks (GNNs) have been successful in modeling graph-structured data. However, similar to other machine learning models, GNNs can exhibit bias in predictions based o…
Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
Yumeng Wang, Zengyi Wo, Wenjun Wang +2
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-w…
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Yudan Song, Yuecen Wei, Yuhang Lu +6
Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improve…