Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models

Guilherme Oliveira Campos1, 2, Edré Moreira1, Wagner Meira Jr1 and Arthur Zimek2

  1. Federal University of Minas Gerais
    Belo Horizonte, Minas Gerais, Brazil
    {gocampos,edre,meira}@dcc.ufmg.br
  2. University of Southern Denmark
    Odense, Denmark
    zimek@imada.sdu.dk

Abstract

Several 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.

Key words

outlier detection, multiple graph models, ensemble

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS123456789X

Publication information

Volume 16, Issue 2 (June 2019)
Year of Publication: 2019
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Campos, G. O., Moreira, E., Jr, W. M., Zimek, A.: Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science and Information Systems, Vol. 16, No. 2, 565-595. (2019), https://doi.org/10.2298/CSIS123456789X