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

Jianhao Guo

doi:10.1109/TKDE.2023.3328645

2024-07-01

PRE

DOI：10.1109/TKDE.2023.3328645

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摘要

En 中文

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.

Keyword:

Noise measurement

Anomaly detection

Image edge detection

Task analysis

Training

Representation learning

Data models

dynamic graphs

learning with noisy labels