Clustering Multilayer Networks
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Detecting community structure is an important methodology to study complex networks. Community detection methods can be divided into two main categories: partitioning methods and overlapping clustering methods. In partitioning methods, each node can belong to at most one community while overlapping clustering methods allow communities with overlapping nodes as well. Community detection is not only a problem in single networks today, but also in multilayer networks where several networks with the same participants are considered at the same time. In recent years, several methods have been proposed for recognizing communities in multilayer networks; however, none of these methods finds overlapping communities. On the other hand, in many types of systems, this approach is not realistic. For example, in social networks, individuals communicate with different groups of people, like friends, colleagues, and family, and this determine overlaps between communities, while they also communicate through several networks like Facebook, Twitter, etc. The overall purpose of this study was to introduce a method for finding overlapping communities in multilayer networks. The proposed method is an extension of the popular Clique Percolation Method (CPM) for simple networks. It has been shown that the structure of communities is dependent on the definition of cliques in multilayer networks which are the smallest components of communities in CPM, and therefore, several types of communities can be defined based on different definitions of cliques. As the most conventional definition of communities, it is necessary for all nodes to be densely connected in single networks to form a community in the multilayer network. In the last part of the thesis, a method has been proposed for finding these types of communities in multilayer networks.
Place, publisher, year, edition, pages
2016. , 77 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-279745OAI: oai:DiVA.org:uu-279745DiVA: diva2:908817
Master Programme in Computer Science
Ashcroft, MichaelNgai, Edith