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cs.LG2025
Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective
Xiao Yang, Xuejiao Zhao, Zhiqi Shen
Graph neural networks (GNNs) have achieved remarkable success in node classification. Building on this progress, heterogeneous graph neural networks (HGNNs) integrate relation type…
cs.LG2024
A Generalizable Anomaly Detection Method in Dynamic Graphs
Xiao Yang, Xuejiao Zhao, Zhiqi Shen
Anomaly detection aims to identify deviations from normal patterns within data. This task is particularly crucial in dynamic graphs, which are common in applications like social ne…