Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2023

Abstract

Abstract Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost which limits them to small-scale networks only; (b) existing treatment of subgraphs produces suboptimal detection accuracy; and (c) unable to provide an explanation as to why a node is anomalous, once it is identified. We identify that the root cause of these weaknesses is a lack of a proper treatment for subgraphs. A treatment called Subgraph Centralization for graph anomaly detection is proposed to address all the above weaknesses. Its importance is shown in two ways. First, we present a simple yet effective new framework called Graph-Centric Anomaly Detection (GCAD). The key advantages of GCAD over existing detectors including deep-learning detectors are: (i) better anomaly detection accuracy; (ii) linear time complexity with respect to the number of nodes; and (iii) it is a generic framework that admits an existing point anomaly detector to be used to detect node anomalies in a network. Second, we show that Subgraph Centralization can be incorporated into two existing detectors to overcome the above-mentioned weaknesses.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), Minneapolis, Minnesota, USA, April 27-29

First Page

703

Last Page

711

Identifier

10.1137/1.9781611977653.ch79

Publisher

Society for Industrial and Applied Mathematics

City or Country

Minneapolis, Minnesota, U.S.

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1137/1.9781611977653.ch79

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