SpeCAE: Spectral autoencoder for anomaly detection in attributed networks

Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Anomaly detection in attributed networks (instance-to-instance dependencies and interactions are available) has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework - SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. Experiments on real-world datasets demonstrate the effectiveness of the proposed SpecAE.

Original languageEnglish
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2233-2236
Number of pages4
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 3 Nov 2019
Externally publishedYes
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period3/11/197/11/19

Keywords

  • Anomaly Detection
  • Network Embedding
  • Neural Networks

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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