3 papers
cs.LG2025
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
Qingfeng Chen, Haojin Zeng, Jingyi Jie +2
With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graph…
cs.LG2025
Learning Fair Graph Representations with Multi-view Information Bottleneck
Chuxun Liu, Debo Cheng, Qingfeng Chen +3
Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory at…
cs.IR2025
A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Accurately predicting counterfactual user feedback is essential for building effective recommender systems. However, latent confounding bias can obscure the true causal relationshi…