Incremental commute time using random walks and online anomaly detection

Nguyen Lu Dang Khoa, Sanjay Chawla

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

1 Citation (Scopus)

Abstract

Commute time is a random walk based metric on graphs and has found widespread successful applications in many application domains. However, the computation of the commute time is expensive, involving the eigen decomposition of the graph Laplacian matrix. There has been effort to approximate the commute time in offline mode. Our interest is inspired by the use of commute time in online mode. We propose an accurate and efficient approximation for computing the commute time in an incremental fashion in order to facilitate real-time applications. An online anomaly detection technique is designed where the commute time of each new arriving data point to any data point in the current graph can be estimated in constant time ensuring a real-time response. The proposed approach shows its high accuracy and efficiency in many synthetic and real datasets and takes only 8 milliseconds on average to detect anomalies online on the DBLP graph which has more than 600,000 nodes and 2 millions edges.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
PublisherSpringer Verlag
Pages49-64
Number of pages16
Volume9851 LNAI
ISBN (Print)9783319461274
DOIs
Publication statusPublished - 2016
Event15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy
Duration: 19 Sep 201623 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9851 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
CountryItaly
CityRiva del Garda
Period19/9/1623/9/16

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Keywords

  • Commute time
  • Incremental learning
  • Online anomaly detection
  • Random walk

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Khoa, N. L. D., & Chawla, S. (2016). Incremental commute time using random walks and online anomaly detection. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings (Vol. 9851 LNAI, pp. 49-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9851 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-46128-1_4