Dynamic Social Network Anomalous Behavior Detection Based on Spatiotemporal Multi-view Graph Attention Fusion Network

Abstract

The development of online social networks is accompanied by intricate abnormal interaction phenomena severely impairing the ecosystem's credibility. Current anomaly detection approaches find it challenging to balance accuracy and robustness when tackling dynamic structural changes, heterogeneous relationships, and lack of labeled data. To address these challenges, this paper proposes ST-MVAN, a Spatio-Temporal Multi-View Attention Network for unsupervised anomaly detection. The proposed framework integrates three core components: (1) in the spatial dimension, we construct heterogeneous relational subgraphs and design an improved Graph Convolutional Network (GCN) that incorporates edge attributes as additive bias and leverages sparse attention to filter structural noise; (2) for feature fusion, an Efficient Channel Attention (ECA) mechanism is introduced to adaptively assign importance weights to multi-view features; and (3) in the temporal dimension, a bidirectional GRU captures dynamic evolutionary dependencies. Finally, a joint Encoder-Decoder framework calculates anomaly scores based on reconstruction errors. Furthermore, we perform experiments on the Digg and Yelp datasets to validate that our method achieves an AUC improvement of up to 12.26% compared to baseline methods.