Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Abstract: Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of link-level anomalies and graph representation learning for identifying categorically anomalous links in these graphs. First, we introduce a fine-grained taxonomy for edge-level anomalies leveraging structural, temporal, and contextual graph properties. Based on these properties, we introduce a method for generating and injecting typed anomalies into graphs. Next, we introduce a novel method to generate continuous-time dynamic graphs featuring consistencies across either or combinations of time, structure, and context. To improve the capabilities of temporal graph methods in learning to detect specific types of anomalous links rather than the bare existence of a link, we extend the generic link prediction setting by: (1) conditioning link existence on contextual edge attributes; and (2) refining the training regime to accommodate diverse perturbations in the negative edge sampler. Building on this, we benchmark methods for typed anomaly detection. Comprehensive experiments on synthetic and real-world datasets -- featuring synthetic and labeled organic anomalies and employing six state-of-the-art learning methods -- validate our taxonomy and generation processes for anomalies and benign graphs, as well as our approach to adapting link prediction methods for anomaly detection. Our results further reveal that different learning methods excel in capturing different aspects of graph normality and detecting different types of anomalies. We conclude with a comprehensive list of findings highlighting opportunities for future research. The code is available at https://anonymous.4open.science/r/TGB-link-anomaly-detection-anonymous-CBF1.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=lcXI5h2JI5
Changes Since Last Submission: Fixed issues with spacing.
Assigned Action Editor: ~Giannis_Nikolentzos1
Submission Number: 2794
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