ABSTRACT
This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.
- S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, 1st ed. Cambridge: Cambridge University Press, Nov. 1994.Google ScholarCross Ref
- A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, "Impact of Human Mobility on Opportunistic Forwarding Algorithms," IEEE Transactions on Mobile Computing, vol. 6, no. 6, pp. 606--620, Jun. 2007. Google ScholarDigital Library
- N. Vastardis and K. Yang, "Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges," IEEE Communications Surveys Tutorials, vol. 15, no. 3, pp. 1355--1371, 2013.Google ScholarCross Ref
- K. Lee, P. Hui, and S. Chong, "Mobility Models in Opportunistic Networks," in Mobile Ad Hoc Networking, S. Basagni, M. Conti, S. Giordano, and I. Stojmenovic, Eds. John Wiley & Sons, Inc., 2013, pp. 360--418.Google Scholar
- P. Hui, E. Yoneki, S. Y. Chan, and J. Crowcroft, "Distributed Community Detection in Delay Tolerant Networks," in Proc. of the 2007 ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture, ser. MobiArch '07. New York, NY, USA: ACM, 2007, pp. 7:1--7:8. Google ScholarDigital Library
- P. Hui, J. Crowcroft, and E. Yoneki, "BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks," IEEE Transactions on Mobile Computing, vol. 10, no. 11, pp. 1576--1589, Nov. 2011. Google ScholarDigital Library
- M. Laibowitz, J. Gips, R. Aylward, A. Pentland, and J. Paradiso, "A sensor network for social dynamics," in Proc. of the 2006 Information Processing in Sensor Networks (ISPN), 2006, pp. 483--491. Google ScholarDigital Library
- J. A. Paradiso, J. Gips, M. Laibowitz, S. Sadi, D. Merrill, R. Aylward, P. Maes, and A. Pentland, "Identifying and Facilitating Social Interaction with a Wearable Wireless Sensor Network," Personal Ubiquitous Computing, vol. 14, no. 2, pp. 137--152, 2010. Google ScholarDigital Library
- F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, "Defining and identifying communities in networks," Proc. of the National Academy of Sciences, vol. 101, no. 9, pp. 2658--2663, Mar. 2004.Google ScholarCross Ref
- S. Fortunato, "Community detection in graphs," Physics Reports-Review Section of Physics Letters, pp. 75--174, Jun. 2009.Google Scholar
- B. W. Kernighan and S. Lin, "An Efficient Heuristic Procedure for Partitioning Graphs," Bell System Technical Journal, vol. 49, no. 2, pp. 291--307, 1970.Google ScholarCross Ref
- C. Bron and J. Kerbosch, "Algorithm 457: finding all cliques of an undirected graph," Communications of the ACM, vol. 16, no. 9, pp. 575--577, Sep. 1973. Google ScholarDigital Library
- L. Freeman, "A Set of Measures of Centrality Based on Betweenness," Sociometry, vol. 40, no. 1, pp. 35--41, Mar. 1977.Google ScholarCross Ref
- M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," Proc. of the National Academy of Sciences, vol. 99, no. 12, pp. 7821--7826, Jun. 2002.Google ScholarCross Ref
- M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical Review E, vol. 69, no. 2, p. 026113, 2004.Google ScholarCross Ref
- A. Clauset, M. E. J. Newman, and C. Moore, "Finding community structure in very large networks," Physical Review E, vol. 70, no. 6, p. 066111, 2004.Google ScholarCross Ref
- V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, p. P10008, Oct. 2008.Google ScholarCross Ref
- R. Aldecoa and I. Marín, "Deciphering Network Community Structure by Surprise," PLoS ONE, vol. 6, no. 9, p. e24195, Sep. 2011.Google ScholarCross Ref
- S. Fortunato and M. Barthélemy, "Resolution limit in community detection," Proc. of the National Academy of Sciences, vol. 104, no. 1, pp. 36--41, 2007.Google ScholarCross Ref
- P. D. Hoff, A. E. Raftery, and M. S. Handcock, "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, vol. 97, no. 460, pp. 1090--1098, 2002.Google ScholarCross Ref
- M. Rosvall and C. T. Bergstrom, "Maps of random walks on complex networks reveal community structure," Proc. of the National Academy of Sciences, vol. 105, no. 4, pp. 1118--1123, Jan. 2008.Google ScholarCross Ref
- R. Cazabet and F. Amblard, "Simulate to Detect: A Multi-agent System for Community Detection," in Proc. of the 2011 IEEE/WIC/ACM Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 2, 2011, pp. 402--408. Google ScholarDigital Library
- P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot, "Pocket Switched Networks and Human Mobility in Conference Environments," in Proc. of the 2005 ACM SIGCOMM Workshop on Delay-tolerant Networking, ser. WDTN '05. New York, NY, USA: ACM, 2005, pp. 244--251. Google ScholarDigital Library
- A. Clauset, "Finding local community structure in networks," Physical Review E, vol. 72, no. 2, p. 026132, Aug. 2005.Google ScholarCross Ref
- U. N. Raghavan, R. Albert, and S. Kumara, "Near linear time algorithm to detect community structures in large-scale networks," Physical Review E, vol. 76, no. 3, p. 036106, Sep. 2007.Google ScholarCross Ref
- R. Kanawati, "Seed-centric approaches for community detection in complex networks," in Social Computing and Social Media. Springer, 2014, pp. 197--208.Google ScholarDigital Library
- A.-L. Barabási and R. Albert, "Emergence of Scaling in Random Networks," Science, vol. 286, no. 5439, pp. 509--512, Oct. 1999.Google ScholarCross Ref
- L. Hubert and P. Arabie, "Comparing partitions," Journal of Classification, vol. 2, no. 1, pp. 193--218, Dec. 1985.Google ScholarCross Ref
- A. Strehl and J. Ghosh, "Cluster Ensembles a Knowledge Reuse Framework for Combining Multiple Partitions," Journal of Machine Learning Research, vol. 3, pp. 583--617, Mar. 2003. Google ScholarDigital Library
- A. Lancichinetti, S. Fortunato, and F. Radicchi, "Benchmark graphs for testing community detection algorithms," Physical Review E, vol. 78, no. 4, p. 046110, Oct. 2008.Google ScholarCross Ref
- D. Hric, R. K. Darst, and S. Fortunato, "Community detection in networks: Structural communities versus ground truth," Physical Review E, vol. 90, no. 6, p. 062805, Dec. 2014.Google ScholarCross Ref
- W. Zachary, "An information flow model for conflict and fission in small groups," Journal of Anthropological Research, vol. 33, no. 4, pp. 452--473, 1977.Google ScholarCross Ref
- J. Yang and J. Leskovec, "Defining and evaluating network communities based on ground-truth," Knowledge and Information Systems, vol. 42, no. 1, pp. 181--213, Oct. 2013. Google ScholarDigital Library
- J. Leskovec and A. Krevl, "SNAP Datasets: Stanford Large Network Dataset Collection," Jun. 2014. {Online}. Available: http://snap.stanford.edu/dataGoogle Scholar
- Fast community structure local uncovering by independent vertex-centred process
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