Conference Proceeding Article
Modern social networks often consist of multiple relationsamong individuals. Understanding the structureof such multi-relational network is essential. In sociology,one way of structural analysis is to identify differentpositions and roles using blockmodels. In thispaper, we generalize stochastic blockmodels to GeneralizedStochastic Blockmodels (GSBM) for performing positionaland role analysis on multi-relational networks.Our GSBM generalizes many different kinds of MultivariateProbability Distribution Function (MVPDF) tomodel different kinds of multi-relational networks. Inparticular, we propose to use multivariate Poisson distributionfor multi-relational social networks. Our experimentsshow that GSBM is able to identify the structuresfor both synthetic and real world network data.These structures can further be used for predicting relationshipsbetween individuals.
Databases and Information Systems | Social Media
Data Management and Analytics
Proceedings of the 2012 SIAM International Conference on Data Mining; California, USA, 2012 April 26-28
Society for Industrial and Applied Mathematics
City or Country
DAI, Bing Tian; CHUA, Freddy Chong Tat; and LIM, Ee-peng.
Structural analysis in multi-relational social networks. (2012). Proceedings of the 2012 SIAM International Conference on Data Mining; California, USA, 2012 April 26-28. 451-462. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3718
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