Renqiang Luo
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View article: Graph Learning
Graph Learning Open
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 significant momentum with the advent of graph neural networ…
View article: Factor Graph-based Interpretable Neural Networks
Factor Graph-based Interpretable Neural Networks Open
Comprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existing solutions generally mitigate the impact of perturbations …
View article: Graph Learning
Graph Learning Open
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 significant momentum with the advent of graph neural networ…
View article: FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks Open
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 through the lens of spectral graph theory, aiming to reconci…
View article: FairGT: A Fairness-aware Graph Transformer
FairGT: A Fairness-aware Graph Transformer Open
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conv…
View article: Algorithmic Fairness: A Tolerance Perspective
Algorithmic Fairness: A Tolerance Perspective Open
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns h…