uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Clustering attributed graphs: models, measures and methods
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.ORCID iD: 0000-0002-3437-9018
2015 (English)In: Network Science, ISSN 2050-1242, Vol. 3, no 3, 408-444 p.Article in journal (Refereed) Published
Abstract [en]

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

Place, publisher, year, edition, pages
2015. Vol. 3, no 3, 408-444 p.
Keyword [en]
attributed graph; multilayer network; clustering; community detection
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-266849DOI: 10.1017/nws.2015.9ISI: 000365009300007OAI: oai:DiVA.org:uu-266849DiVA: diva2:868914
Available from: 2015-11-12 Created: 2015-11-12 Last updated: 2016-01-01Bibliographically approved

Open Access in DiVA

fulltext(719 kB)36 downloads
File information
File name FULLTEXT02.pdfFile size 719 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Magnani, Matteo
By organisation
Computing Science
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 36 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 313 hits
ReferencesLink to record
Permanent link

Direct link