RustGraph: Robust Anomaly Detection in Dynamic Graphs by Jointly Learning Structural-Temporal Dependency

• Published in: IEEE transactions on knowledge and data engineering Vol. 36; no. 7; pp. 3472 - 3485
• Main Authors: Guo, Jianhao, Tang, Siliang, Li, Juncheng, Pan, Kaihang, Wu, Lingfei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; Data models; dynamic graphs; Graphs; Image edge detection; Labels; Learning; learning with noisy labels; Noise measurement; Representation learning; Robustness; Social networks; Task analysis; Training
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3328645

Dynamic graph-based data are ubiquitous in the real world, such as social networks, finance systems, and traffic flow. Fast and accurately detecting anomalies in these dynamic graphs is of vital importance. However, despite promising results the current anomaly detection methods have achieved, there are two major limitations when coping with dynamic graphs. The first limitation is that the topological structures and the temporal dynamics have been modeled separately, resulting in less expressive features for detection. The second limitation is that the models have been trained by unreliable noisy labels generated by random negative sampling, rendering it severely vulnerable to subtle perturbations. To overcome the above limitations, we propose RustGraph, a robust anomaly detection framework by jointly learning structural-temporal dependency in dynamic graphs. To this end, we design a variational graph auto-encoder with informative prior that simultaneously encodes both graph structural and temporal information. Then we introduce a fine-grained contrastive learning method to learn better node representations by utilizing the temporal consistency between two snapshots. Furthermore, we formulate the noisy label learning problem for anomaly detection in dynamic graph, and then propose a robust anomaly detector to improve the model performance by leveraging learned graph structure signal. Our extensive experiments on six real-world datasets demonstrate the proposed RustGraph method achieves state-of-the-art performance with an average of 3.64% improvement on AUC-ROC metric compared with all baselines. The codes are available at https://github.com/aubreygjh/RustGraph .