6 citations · 6 across the 1 of their papers we have counts for
3 papers
cs.LG2025
Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
Yiming Xu, Zhen Peng, Bin Shi +4
The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-base…
cs.LG2025
Out-of-Distribution Generalization on Graphs via Progressive Inference
Yiming Xu, Bin Shi, Zhen Peng +3
The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untena…
cs.IR2024★ 6 cited
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
Linxin Guo, Yaochen Zhu, Min Gao +3
Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiv…