4 papers
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…
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…
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…
Mitigating Dual Latent Confounding Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditiona…