Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

Jiuchuan Jiang, Manfred Jaeger

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)


Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes. In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models.
Original languageEnglish
Title of host publicationFoundations of Intelligent Systems : 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings
EditorsTroels Andreasen, Henning Christiansen, Juan-Carlos Cubero, Zbigniew W. Raś
PublisherSpringer Publishing Company
Publication date2014
ISBN (Electronic)978-3-319-08326-1
Publication statusPublished - 2014
SeriesLecture Notes in Computer Science

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