KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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K-Hop Community Search Based On Local Distance Dynamics

  • Meng, Tao (College of Information Science and Engineering, Hunan University) ;
  • Cai, Lijun (College of Information Science and Engineering, Hunan University) ;
  • He, Tingqin (College of Information Science and Engineering, Hunan University) ;
  • Chen, Lei (College of Electrical and Information Engineering, Hunan University) ;
  • Deng, Ziyun (Department of Economics and Trade, ChangSha Commerce and Tourism College)
  • Received : 2017.11.12
  • Accepted : 2018.03.15
  • Published : 2018.07.31
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Abstract

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.

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References

  1. Xu X, Yuruk N and Feng Z, "Scan: a structural clustering algorithm for networks," in Proc. of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.824-833, August, 12-15, 2007.
  2. Newman M E J, "Modularity and community structure in networks," Proceedings of the national academy of sciences, vol.103, no.23, pp.8577-8582, June, 2006. https://doi.org/10.1073/pnas.0601602103
  3. Shao J, Han Z, Yang Q and Zhou T, "Community detection based on distance dynamics," in Proc. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1075-1084, August, 10-13, 2015.
  4. Raghavan U N, Albert R and Kumara S, "Near linear time algorithm to detect community structures in large-scale networks," Physical review E, vol.76, no.3, pp.36-106, September, 2007.
  5. Newman M E J and Girvan M, "Finding and evaluating community structure in networks," Physical review E, vol.69, no.2, pp.26-113, February, 2004.
  6. Sozio M and Gionis A, "The community-search problem and how to plan a successful cocktail party," in Proc. of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.939-948, July, 25-28, 2010.
  7. Ugander J, Backstrom L and Marlow C, "Structural diversity in social contagion," Proceedings of the National Academy of Sciences, vol.109, no.16, pp.5962-5966, April, 2012. https://doi.org/10.1073/pnas.1116502109
  8. Zhang S, Liu Q and Lin Y, "Anonymizing popularity in online social networks with full utility," Future Generation Computer Systems, vol.72, no.1, pp.227-238, July, 2017. https://doi.org/10.1016/j.future.2016.05.007
  9. Cui W, Xiao Y, Wang H and Wang W, "Local search of communities in large graphs," in Proc. of the 2014 ACM SIGMOD International Conference on Management of Data, pp.991-1002, June, 22-27, 2014.
  10. Li R H, Qin L and Yu J X, "Influential community search in large networks," VLDB Endowment, vol.8, no.5, pp.509-520, January, 2015. https://doi.org/10.14778/2735479.2735484
  11. Huang X, Cheng H, Qin L and Tian W, "Querying k-truss community in large and dynamic graphs," in Proc. of the 2014 ACM SIGMOD International Conference on Management of Data, pp.1311-1322, June, 22-27, 2014.
  12. Huang X, Lakshmanan L V S and Yu J X, "Approximate closest community search in networks," VLDB Endowment, vol.9, no.4, pp.276-287, December, 2015. https://doi.org/10.14778/2856318.2856323
  13. Wu Y, Jin R, Li J and Zang X, "Robust local community detection: on free rider effect and its elimination," VLDB Endowment, vol.8, no.7, pp.798-809, February, 2015. https://doi.org/10.14778/2752939.2752948
  14. Xie J, Kelley S and Szymanski B K, "Overlapping community detection in networks: The state-of-the-art and comparative study," ACM Computing Surveys (CSUR), vol.45, no.43, pp.1-35, August, 2013.
  15. Palla G, Derenyi I and Farkas I, "Uncovering the overlapping community structure of complex networks in nature and society," Nature, vol.435, no.7043, pp.814-818, June, 2005. https://doi.org/10.1038/nature03607
  16. Lancichinetti A, Fortunato S and Kertesz J, "Detecting the overlapping and hierarchical community structure in complex networks," New Journal of Physics, vol.11, no.3, pp.15-33, March, 2009.
  17. Fortunato S, "Community detection in graphs," Physics reports, vol.486, no.3, pp.75-174, February, 2010. https://doi.org/10.1016/j.physrep.2009.11.002
  18. Huang J, Sun H, and Liu Y, "Towards online multiresolution community detection in large-scale networks," PloS one, vol.6, no.8, pp. e23829, August, 2011. https://doi.org/10.1371/journal.pone.0023829
  19. Cheng J, Zhu L and Ke Y, "Fast algorithms for maximal clique enumeration with limited memory," in Proc. of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1240-1248, August, 12-16, 2012.
  20. Zhou X, Li K and Xiao G, "Top k Favorite Probabilistic Products Queries," IEEE Transactions on Knowledge and Data Engineering, vol.28, no.10, pp.2808-2821, June, 2016. https://doi.org/10.1109/TKDE.2016.2584606
  21. Wang J and Cheng J, "Truss decomposition in massive networks," VLDB Endowment, vol.5, no.9, pp.812-823, May, 2012. https://doi.org/10.14778/2311906.2311909
  22. Chang L, Yu J X and Qin L, "Efficiently computing k-edge connected components via graph decomposition," in Proc. of the 2013 ACM SIGMOD International Conference on Management of Data, pp.205-216, June, 2013.
  23. Barahona M and Pecora L M, "Synchronization in small-world systems," Physical review letters, vol.89, no.5, pp.54-101, July, 2002.
  24. Watts D J and Strogatz S H, "Collective dynamics of 'small-world' networks," nature, vol.393, no.6684, pp.440-442, June, 1998. https://doi.org/10.1038/30918
  25. Liu Q, Wang G, and Li F, "Preserving privacy with probabilistic indistinguishability in weighted social networks," IEEE Transactions on Parallel and Distributed Systems, vol.28, no.5, pp.1417-1429, May, 2017. https://doi.org/10.1109/TPDS.2016.2615020
  26. Kunze M, Weidlich M and Weske M, "Behavioral similarity-a proper metric," in Proc. of the 2011 Springer Berlin Heidelberg International Conference on Business Process Management, pp.166-181, August, 2011.
  27. Lipkus A H, "A proof of the triangle inequality for the Tanimoto distance," Journal of Mathematical Chemistry, vol.26, no.3, pp.263-265, October, 1999. https://doi.org/10.1023/A:1019154432472
  28. Edachery J, Sen A and Brandenburg F J, "Graph clustering using distance-k cliques," in Proc. of the 7th International Symposium on Graph Drawing, pp.98-106, March, 1999.
  29. Xiao G, Li K, and Li K, "Efficient top-(k, l) range query processing for uncertain data based on multicore architectures," Distributed and Parallel Databases, vol.33, no.3, pp.381-413, October, 2015. https://doi.org/10.1007/s10619-014-7156-8
  30. Luo J and Wang T, "Motif discovery using an immune genetic algorithm," Journal of theoretical biology, vol.264, no.2, pp.319-325, May, 2010. https://doi.org/10.1016/j.jtbi.2010.02.010

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