MMP: Enhancing unsupervised graph anomaly detection with multi-view message passing

The complementary and conflicting relationships between views are two fundamental issues when applying Graph Neural Networks (GNNs) to multi-view attributed graph anomaly detection. Most existing approaches do not address the inherent multi-view properties in the attribute space or leverage complementary information through simple representation fusion, which overlooks the conflicting information among different views. In this paper, we argue that effectively applying GNNs to multi-view anomaly detection necessitates reinforcing complementary information between views and, more importantly, managing conflicting information. Building on this perspective, this paper introduces Multi-View Message Passing (MMP), a novel and effective message passing paradigm specifically designed for multi-view anomaly detection. In the multi-view aggregation phase of MMP, views containing different types of information are integrated using view-specific aggregation functions. This approach enables the model to dynamically adjust the amount of information aggregated from complementary and conflicting views, thereby mitigating issues arising from insufficient complementary information and excessive conflicting information, which can lead to suboptimal representation learning. Furthermore, we propose an innovative aggregation loss mechanism that enhances model performance by optimizing the reconstruction differences between aggregated representations and the original views, thereby improving both detection accuracy and model interpretability. Extensive experiments on synthetic and real-world datasets validate the effectiveness and robustness of our method. The source code is available at https://github.com/weihus/MMP.