5 papers
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
Sheng'en Li, Dongmian Zou
Online link recommendation on evolving graphs is performative: by choosing which candidate links to show users, the system changes which links form and what feedback it later obser…
Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li +1
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieve…
Klein Model for Hyperbolic Neural Networks
Yidan Mao, Jing Gu, Marcus C. Werner +1
Hyperbolic neural networks (HNNs) have been proved effective in modeling complex data structures. However, previous works mainly focused on the Poincaré ball model and the hyperbol…
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks
Jing Gu, Dongmian Zou
Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, exist…
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
Yifei Yang, Peng Wang, Xiaofan He +1
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often la…