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High-performance implementations of a clustering algorithm for finding network communities

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6 Author(s)
Restrepo, A. ; Sch. of Comput., Grand Valley State Univ., Allendale, MI, USA ; Solano, A. ; Scripps, J. ; Trefftz, C.
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The size and interconnectedness of social networks continues to increase. As a result, finding communities or subsets of like nodes within these large networks has become a resource-intensive endeavor. In this paper, we characterize community-finding organized on the basis of network/set properties, and describe an agglomerative algorithm called egocentric community finding. The primary contribution of this paper is a performance evaluation in which the egocentric data-mining algorithm is optimized for execution on various computing platforms, including GPU's, multicore systems, and large-scale distributed systems.

Published in:

Electro/Information Technology (EIT), 2012 IEEE International Conference on

Date of Conference:

6-8 May 2012