AD-TIN: Edge Anomaly Detection for Temporal Interaction Networks using Multi-representation Attention

Anomaly detection in temporal interaction networks (TINs) has become critical in network security, digital finance, and social networks. While recent studies based on Graph Neural Networks (GNNs) have yielded promising results, the existing methods are still limited by insufficient labels and noisy data, often ignoring the information filtering for unrelated user interactions. Therefore, this paper proposes a dynamic edge anomaly detection framework, AD-TIN, to address these challenges based on a multi-representation attention mechanism. It encodes graph structural information using a network information propagation module with neighbor sampling and graph diffusion. Furthermore, the network update module combines past node states with current structural features to capture the temporal information in potential user relationships, effectively mitigating the impact of noisy data. Extensive experiments on three real-world datasets demonstrate the robustness and efficacy of AD-TIN in addressing noise and unrelated interactions for edge anomaly detection.