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Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Stochastic network motif detection in social media

Language

English

Abstract

Network motifs refer to recurrent patterns of interconnections which are found to be over-represented in real networks when compared with random ones. Such basic building blocks can well characterize the structure of complex networks. Extending the building blocks to stochastic ones allows for more robust motif detection networks which are stochastic in nature. Network motif analysis, commonly adopted in bioinformatics, has recently been applied to also online social media. In this paper, we propose to detect stochastic network motifs in social media with the conjecture that social interactions are of stochastic nature. In particular, we apply a stochastic motif detection algorithm based on the finite mixture model to both synthesized datasets and real online datasets to evaluate the effectiveness. Also, we discuss how the obtained stochastic motifs could be interpreted and compared qualitatively with some of the results obtained from others which are recently reported in the literature. © 2011 IEEE.

Keywords

Expectation-maximization algorithm, Mixture model, Social networks, Stochastic network motifs

Publication Date

2011

Source Publication Title

Proceedings: 11th IEEE International Conference on Data Mining Workshops

Start Page

949

End Page

956

Conference Location

Vancouver, Canada

Publisher

IEEE

ISBN (print)

9781467300056

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