Attribute Graph Node Anomaly Detection Based on Training Strategy Optimization

Attributional network anomaly detection is increasingly becoming a focal point of academic research due to its applications in critical domains like social networking and financial fraud. This task faces many challenges due to the differences in attributes between anomalous nodes and others, as well as their complex interactions. While existing shallow methods fall short in concurrently addressing both attributes and structures, leading to suboptimal performance, methods based on deep learning have made significant advances in enhancing anomaly detection. However, they still lack sufficient exploitation of anomaly information. In response to the aforementioned issue, this paper introduces an attribute graph node anomaly detection method based on training strategy optimization (ANATSO). The model constructs a secondary view through edge perturbation and samples subgraphs from each view. These subgraphs are then used to initialize networks for subgraph-node and node-node instance pairs. Additionally, a phased training process is implemented, where the outcomes from the initial phase are used to optimize the node input processing in the subsequent phase, ultimately calculating anomaly scores for each node. This research was evaluated on five benchmark datasets, and the results demonstrate that compared to traditional baseline methods, our model significantly improves the precision of anomaly detection.