SpeCAE: Spectral autoencoder for anomaly detection in attributed networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations

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 (US)
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
StatePublished - Nov 3 2019
Externally publishedYes
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Country/TerritoryChina
CityBeijing
Period11/3/1911/7/19

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Business, Management and Accounting

Keywords

  • Anomaly Detection
  • Network Embedding
  • Neural Networks

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