2014 | OriginalPaper | Buchkapitel
A Unified Model for Community Detection of Multiplex Networks
verfasst von : Guangyao Zhu, Kan Li
Erschienen in: Web Information Systems Engineering – WISE 2014
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Multiplex networks contain multiple simplex networks. Community detection of multiplex networks needs to deal with information from all the simplex networks. Most approaches aggregate all the links in different simplex networks treating them as being equivalent. However, such aggregation might ignore information of importance in simplex networks. In addition, for each simplex network, the aggregation only considers adjacency relation among nodes, which can’t reflect real closeness among nodes very well. In order to solve the problems above, this paper presents a unified model to detect community structure by grouping the nodes based on a unified matrix transferred from multiplex network. In particular, we define importance and node similarity to describe respectively correlation difference of simplex networks and closeness among nodes in each simplex network. The experiment results show the higher accuracy of our model for community detection compared with competing methods on synthetic datasets and real world datasets.