10 citations · 10 across the 19 of their papers we have counts for
10 papers · 1 filter
Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
Qing Qing, Xikun Zhang, Zhongyuan Zhang +7
Aging clocks aim to estimate biological age, a measure of physiological state distinct from chronological age, from observable biomarkers, and are widely used for health assessment…
GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
Jingjing Zhou, Shiyu Huang, Qing Qing +7
Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform governance. However, existing GA…
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
Jingjing Zhou, Yongshuai Yang, Qing Qing +5
Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that existing unlearning strategies t…
FairGU: Fairness-aware Graph Unlearning in Social Networks
Renqiang Luo, Yongshuai Yang, Huafei Huang +6
Graph unlearning has emerged as a critical mechanism for supporting sustainable and privacy-preserving social networks, enabling models to remove the influence of deleted nodes and…
Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren +5
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining…
Fairness in Augmented Graph Learning: A Survey
Renqiang Luo, Huafei Huang, Ziqi Xu +4
Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques. Examples include federated learning, graph transformers,…
Factor Graph-based Interpretable Neural Networks
Yicong Li, Kuanjiu Zhou, Shuo Yu +4
Comprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existin…
FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
Renqiang Luo, Huafei Huang, Ivan Lee +3
Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are co…
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
Renqiang Luo, Huafei Huang, Shuo Yu +4
Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness t…
FairGT: A Fairness-aware Graph Transformer
Renqiang Luo, Huafei Huang, Shuo Yu +2
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph info…