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Community detection in attributed networks based on heterogeneous vertex interactions

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Abstract

Community detection is attracting more attention on social network analysis. It is to cluster densely connected nodes into communities. In attributed networks where nodes have attributes, community detection should take both topology and attributes into account. Traditional community detection algorithms only focus on the topological structure. They do not take advantage of attributes so their performance is limited. Besides, most community detection algorithms for attributed networks are far from satisfactory because of accuracy and algorithm complexity. Moreover, most of the algorithms depend on users to specify the community number, which also impacts the performance. Based on a high-performance community detection algorithm named Attractor, we propose Hetero-Attractor which can detect communities in attributed networks. It expands the sociological model of Attractor and generates a heterogeneous network from the attributed network. Hetero-Attractor analyzes the new network based on the interactions between vertices. By these interactions, the topological information and attribute information not only play a role in the community detection but also interact with each other to reach a balanced result. It also develops a novel way to analyze the heterogeneous network. The experiments demonstrate that our algorithm performs better by utilizing the attribute information, and outperforms other methods both in terms of accuracy as well as scalability, with a maximum promotion of 60% in accuracy.

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References

  1. Adamic L A, Glance N (2005) The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd international workshop on link discovery. ACM, pp 36–43

  2. Cross R, Parker A, Christensen C M, Anthony S D, Roth E A (2004) The hidden power of social networks. J Appl Manag Entrep 9(Oct)

  3. De Nooy W, Mrvar A, Batagelj V (2011) Exploratory social network analysis with Pajek, vol 27. Cambridge University Press

  4. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    Article  MathSciNet  Google Scholar 

  5. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci U S A 104(1):36–41

    Article  Google Scholar 

  6. Gil-Mendieta J, Schmidt S (1996) The political network in Mexico. Soc Networks 18(4):355–381

    Article  Google Scholar 

  7. Girvan M, Newman M E J (2002) Community structure in social and biological networks. Proc Natl Acad Sci U S A 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  8. Grossetti M, Lazega E (2006) The collegial phenomenon: the social mechanisms of cooperation among peers in a corporate law partnership. Legal Ethics 21(8):183–184

    Google Scholar 

  9. Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193–218

    Article  MATH  Google Scholar 

  10. Lancichinetti A, Fortunato S (2009) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E Stat Nonlinear Soft Matter Phys 80(1):016118

    Article  Google Scholar 

  11. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlinear Soft Matter Phys 78(4):046110

    Article  Google Scholar 

  12. Leung IXY, Hui P, Lio P, Crowcroft J (2009) Towards real-time community detection in large networks. Phys Rev E Stat Nonlinear Soft Matter Phys 79(6):066107

    Article  Google Scholar 

  13. Mislove A, Marcon M, Gummadi K P, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement. ACM, pp 29–42

  14. Moody J (2001) Peer influence groups: identifying dense clusters in large networks. Soc Networks 23(4):261–283

    Article  Google Scholar 

  15. Newman M E J, Clauset A (2015) Structure and inference in annotated networks. Comput Sci 136 (2-3):93–100

    Google Scholar 

  16. Newman M E J (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103 (23):8577–8582

    Article  Google Scholar 

  17. Porter M A, Onnela J P, Mucha P J (2009) Communities in networks. Not Am Math Soc 56(9):4294–4303

    MathSciNet  MATH  Google Scholar 

  18. Raghavan U N, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E Stat Nonlinear Soft Matter Phys 76(3):036106

    Article  Google Scholar 

  19. Rand W M (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  20. Rogers E M, Kincaid D L (1982) Communication networks: toward a new paradigm for research. Contemp Sociol 11(2)

  21. Shao J, Han Z, Yang Q, Zhou T (2015) Community detection based on distance dynamics. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1075–1084

  22. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22 (8):888–905

    Article  Google Scholar 

  23. Strehl A, Ghosh J (2002) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617

    MathSciNet  MATH  Google Scholar 

  24. Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery 3(2):1–159

    Article  Google Scholar 

  25. Van Dongen S M (2001) Graph clustering by flow simulation. University of Utrecht, PhD thesis

  26. Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. In: IEEE 13th international conference on data mining (ICDM), 2013. IEEE, pp 1151–1156

  27. Zhang X-K, Fei S, Song C, Tian X, Ao Y-Y (2015) Label propagation algorithm based on local cycles for community detection. Int J Mod Phys B 29(05):1550029

    Article  MathSciNet  Google Scholar 

  28. Zhang Y, Tang J, Yang Z, Pei J, Yu P S (2015) Cosnet: connecting heterogeneous social networks with local and global consistency. In: Proceedings of the 21th ACM SIGKDD internat ional conference on knowledge discovery and data mining. ACM, pp 1485–1494

  29. Zhou Y, Cheng H, Yu J X (2009) Graph clustering based on structural/attribute similarities. Proceedings of the VLDB Endowment 2(1):718–729

    Article  Google Scholar 

  30. Zhou Y, Cheng H, Yu J X (2010) Clustering large attributed graphs: an efficient incremental approach. In: IEEE 10th international conference on data mining (ICDM), 2010. IEEE, pp 689–698

  31. Zhou Y, Liu L (2013) Social influence based clustering of heterogeneous information networks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 338–346

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Acknowledgements

This work is partially supported by the The National Key Research and Development Program of China (2016YFB0200401), by program for New Century Excellent Talents in University, by National Science Foundation (NSF) China 61402492, 61402486, 61379146, by the laboratory pre-research fund (9140C810106150C81001).

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Authors and Affiliations

  1. College of Computer Science, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Xin Wang, Jianglong Song, Kai Lu & Xiaoping Wang

  2. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Xin Wang, Kai Lu & Xiaoping Wang

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  1. Xin Wang
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  2. Jianglong Song
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  3. Kai Lu
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  4. Xiaoping Wang
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Correspondence to Xin Wang.

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Wang, X., Song, J., Lu, K. et al. Community detection in attributed networks based on heterogeneous vertex interactions. Appl Intell 47, 1270–1281 (2017). https://doi.org/10.1007/s10489-017-0948-6

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  • DOI: https://doi.org/10.1007/s10489-017-0948-6

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