Multi-resolution social network community identification and maintenance on big data platform

Date

2013-06-07

Authors

Aksu, Hidayet
Canım, M.
Chang, Y.-C.
Körpeoğlu, İbrahim
Ulusoy, Özgür

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Abstract

Community identification in social networks is of great interest and with dynamic changes to its graph representation and content, the incremental maintenance of community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k values, so that multiple community resolutions are represented with multiple k-core graphs. We then present distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on different topics in rich social network content simultaneously. © 2013 IEEE.

Source Title

2013 IEEE International Congress on Big Data

Publisher

IEEE

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Keywords

Big Data analytics, Community identification, Distributed computing, Dynamic social networks, k-core, Big datum, Community engagement, Community identification, Dynamic social networks, Experimental evaluation, Incremental maintenance, Multiple resolutions, Algorithms, Distributed computer systems, Social networking (online)

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Citation

Published Version (Please cite this version)

Language

English

Type

Conference Paper