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Framework for Fast Identification of Community Structures in Large-Scale Social Networks

  • Yutaka I. Leon-Suematsu
  • Kikuo Yuta
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 12)

Abstract

One of the most important features of real networks is the presence of community structures or the subset of nodes that are densely connected to each other when compared to the rest of the networks, which encode the information about the organization and functionality of the nodes. Social networking sites (SNS), which allow the interaction of millions of users, have important scientific and practical implications; however, they require the development of fast algorithms. We focus on the algorithm developed by Clauset, Newman, and Moore (CNM) and its widely used modifications to analyze the behavior and effectiveness in terms of speed. This chapter describes the inefficiencies of CNM and shows that the determinant factor that impacts the speed is the number of interconnected communities (NIC) that represent the number of operations performed when merging two communities. We propose a new improvement of CNM that considers the NIC and a new implementation framework to accelerate CNM. Our improvements were compared with the former CNM and its variations when applied to large-scale networks from seven real data sets (Mixi, Facebook, Flickr, LiveJournal, Orkut, YouTube, and Delicious) and five synthetic networks with different structural properties. The experimental results demonstrate that the performance of all algorithms is impacted by the structural properties of the network and our proposed improvements outperform former algorithms in terms of speed and modularity in most network structures, thereby showing its applicability to real large-scale networks.

Keywords

Social Networking Site Normalize Mutual Information Lower Triangular Matrix Undirected Network Implementation Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors are grateful to Yoshi Fujiwara from ATR for his ever-inspiring discussions and helpful comments on preliminary versions. We would like to thank the anonymous reviewers for their invaluable comments and for letting us know about the competing algorithm. We also thank Alan Mislove from the Max-Planck Institute for providing his data sets. Finally, we would like to thank Mixi, Inc., for providing the data set, in which users were all encrypted. The data set is handled under a Non-Disclosure Agreement. Our work does not evaluate the personality of participants or services in any SNSs. We declare no competing interests.

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Copyright information

© Springer US 2010

Authors and Affiliations

  1. 1.National Institute of Information and Communications Technology (NiCT)KyotoJapan
  2. 2.Crev Inc.Keihanna-Plaza LaboratoriesKyotoJapan

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