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.
|Title of host publication||Foundations of Intelligent Systems : 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings|
|Editors||Troels Andreasen, Henning Christiansen, Juan-Carlos Cubero, Zbigniew W. Raś|
|Publisher||Springer Publishing Company|
|Publication status||Published - 2014|
|Series||Lecture Notes in Computer Science|
Jiang, J., & Jaeger, M. (2014). Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks. In T. Andreasen, H. Christiansen, J-C. Cubero, & Z. W. Raś (Eds.), Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings (Vol. 8502, pp. 30-39). Springer Publishing Company. Lecture Notes in Computer Science https://doi.org/10.1007/978-3-319-08326-1_4