Outlier Detection in Graphs: On the Impact of Multiple Graph Models

Guilherme Oliveira Campos, Wagner Meira Jr., Arthur Zimek

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Various previous works proposed techniques to detect outliers in graph data. Usually, some complex dataset is modeled as a graph and a technique for detecting outliers in graphs is applied. The impact of the graph model on the outlier detection capabilities of any method has been ignored. Here we assess the impact of the graph model on the outlier detection performance and the gains that may be achieved by using multiple graph models and combining the results obtained by these models. We show that assessing the similarity between graphs may be a guidance to determine effective combinations, as less similar graphs are complementary with respect to outlier information they provide and lead to better outlier detection.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics : WIMS 2018
EditorsCostin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos
Number of pages12
PublisherAssociation for Computing Machinery
Publication date25. Jun 2018
Article number21
ISBN (Electronic)978-1-4503-5489-9
DOIs
Publication statusPublished - 25. Jun 2018
Event8th International Conference on Web Intelligence, Mining and Semantics - Novi Sad, Serbia
Duration: 25. Jun 201827. Jun 2018
https://wims2018.pmf.uns.ac.rs/

Conference

Conference8th International Conference on Web Intelligence, Mining and Semantics
Country/TerritorySerbia
CityNovi Sad
Period25/06/201827/06/2018
Internet address

Keywords

  • Ensemble
  • Multiple graph models
  • Outlier detection

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