Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

Name thesis:degree_name

M.Sc. Mathematics and Statistics

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Mathematics

Grantor

Brock University

Year dc:date.issued

2015

Author dc:creator

• Pichugina, Oksana

• Attributed Social Networks, Community Detection

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/7324

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/7324