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cs.LG2025★ 1 cited
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Yunfeng Zhao, Yixin Liu, Shiyuan Li +3
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. D…
cs.LG2025
Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
Qingfeng Chen, Shiyuan Li, Yixin Liu +3
Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertai…