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

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      Author(s)
      Aksu, Hidayet
      Canım, M.
      Chang, Y.-C.
      Körpeoğlu, İbrahim
      Ulusoy, Özgür
      Date
      2013-06-07
      Source Title
      2013 IEEE International Congress on Big Data
      Publisher
      IEEE
      Pages
      102 - 109
      Language
      English
      Type
      Conference Paper
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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.
      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)
      Permalink
      http://hdl.handle.net/11693/27960
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/BigData.Congress.2013.23
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      • Department of Computer Engineering 1510
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