Publication Type

Conference Proceeding Article

Publication Date

4-2012

Abstract

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.

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Management and Analytics

Publication

Proceedings of the 2012 SIAM International Conference on Data Mining; California, USA, 2012 April 26-28

First Page

451

Last Page

462

ISBN

9781611972320

Identifier

10.1137/1.9781611972825.39

Publisher

Society for Industrial and Applied Mathematics

City or Country

Philadelphia, Pennsylvania

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1137/1.9781611972825.39

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