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Anomaly Detection in Time-Evolving Attributed Networks

  • Xi'an Jiaotong University
  • Arizona State University

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

5 Scopus citations

Abstract

Recently, there is a surge of research interests in finding anomalous nodes upon attributed networks. However, a vast majority of existing methods fail to capture the evolution of the networks properly, as they regard them as static. Meanwhile, they treat all the attributes and the instances equally, ignoring the existence of noisy. To tackle these problems, we propose a novel dynamic anomaly detection framework based on residual analysis, namely AMAD. It leverages the small smooth disturbance between time stamps to characterize the evolution of networks for incrementally update. Experiments conducted on several datasets show the superiority of AMAD in detecting anomalies.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publicationBDMS, BDQM, and GDMA, Proceedings
EditorsJoao Gama, Juggapong Natwichai, Yongxin Tong, Jun Yang, Guoliang Li
PublisherSpringer Verlag
Pages235-239
Number of pages5
ISBN (Print)9783030185893
DOIs
StatePublished - 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: 22 Apr 201925 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11448 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/TerritoryThailand
CityChiang Mai
Period22/04/1925/04/19

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