ENDASh: Embedding Neighbourhood Dissimilarity with Attribute Shuffling for Graph Anomaly Detection

Qizhou Wang, Mahsa Salehi, Jia Shun Low, Wray Buntine, Christopher Leckie

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Recent unsupervised GNN based graph anomaly detection (GAD) methods adopt specific mechanisms designed for anomaly detection. This is in contrast to earlier methods that utilise components such as graph autoencoders that were designed for more general use-cases. However, these newer methods only lead to a modest increase in detection accuracy at the cost of complicated optimisation schemes and higher runtimes. To overcome these issues, we propose Embedding Neighbourhood Dissimilarity (END) with Attribute Shuffling (ENDASh), a simple but scalable and effective GAD framework. ENDASh utilises our proposed END measure to quantify the degree of abnormality of nodes using GraphSAGE embeddings that were optimised with Attribute Shuffling, a data augmentation method designed to project anomalies away from inliers in the latent space. Extensive experiments on real-world benchmarks demonstrate the competitive GAD performance of our ENDASh while being computationally efficient and capable of operating in an inductive environment.

Original languageEnglish
Title of host publication26th Pacific-Asia Conference, PAKDD 2022 Chengdu, China, May 16–19, 2022 Proceedings, Part II
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
Place of PublicationCham Switzerland
PublisherSpringer
Pages17-29
Number of pages13
ISBN (Electronic)9783031059360
ISBN (Print)9783031059353
DOIs
Publication statusPublished - 2022
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2022 - Chengdu, China
Duration: 16 May 202219 May 2022
Conference number: 26th
https://link.springer.com/book/10.1007/978-3-031-05936-0 (Proceedings)
http://www.pakdd.net/ (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13281
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2022
Abbreviated titlePAKDD 2022
Country/TerritoryChina
CityChengdu
Period16/05/2219/05/22
Internet address

Keywords

  • Anomaly detection
  • Attributed graph
  • Graph mining

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