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International Conference on Web-Age Information Management

WAIM 2016: Web-Age Information Management pp 353–364Cite as

Collaborative Partitioning for Multiple Social Networks with Anchor Nodes

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

Abstract

Plenty of individuals are getting involved in more than one social networks, and maintaining multiple relationships of social networks. The value behind the integrated information of multiple social networks is high. Howerver, the research of multiple social networks has been less studied. Our work presented in this paper taps into abundant information of multiple social networks and aims to resolve the initial phase problem of multi-related social network analysis based on MapReduce by partition the mutli-related social networks into non-intersecting subsets. To concretize our discussion, we propose a new multilevel framework (CPMN), which usually proceed in four stages, Merging Phase, Coarsening Phase, Intial Partitioning Phase and Uncoarsening Phase. We propose a modified matching strategy in the second stage and a modified refinement algorithm in the fourth stage. We prove the effective of CPMN on both synthetic data and real datasets. Experiments show that the same node in different social networks is assigned to the same partition by 100 % without sacrificing the load balance and edge-cut too much. We believe that our work will shed light on the study of multiple social networks based on MapReduce.

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References

  1. Zhang, J., Yu, P.S.: MCD: Mutual clustering across multiple heterogeneous networks. In: IEEE BigData Congress (2015)

    Google Scholar 

  2. Zhang, J., Yu, P.S.: Mutual community detection across multiple partially aligned social networks. arXiv preprint arXiv:1506.05529 (2015)

  3. Zhang, J., Philip, S.Y.: Integrated anchor and social link predictions across social networks. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2125–2131. AAAI Press (2015)

    Google Scholar 

  4. Liu, G., Zhang, M., Yan, F.: Large-scale social network analysis based on mapreduce. In: 2010 International Conference on Computational Aspects of Social Networks (CASoN), pp. 487–490. IEEE (2010)

    Google Scholar 

  5. Gong, N.: Using map-reduce for large scale analysis of graph-based data (2011)

    Google Scholar 

  6. Jin, S., Yu, P.S., Li, S., Yang, S.: A parallel community structure mining method in big social networks. Math. Prob. Eng. 2015 (2015)

    Google Scholar 

  7. Feder, T., Hell, P., Klein, S., Motwani, R.: Complexity of graph partition problems. In: Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, pp. 464–472. ACM (1999)

    Google Scholar 

  8. Golberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    Google Scholar 

  9. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  10. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  MATH  Google Scholar 

  11. Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: 19th Conference on Design Automation, pp. 175–181. IEEE (1982)

    Google Scholar 

  12. Hendrickson, B., Leland, R.: A multilevel algorithm for partitioning graphs. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, p. 28. ACM (1995)

    Google Scholar 

  13. Pothen, A., Simon, H.D., Liou, K.P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  14. Karypis, G., Kumar, V.: Multilevel graph partitioning schemes. In: ICPP (3), pp. 113–122 (1995)

    Google Scholar 

  15. Berger, M.J., Bokhari, S.H.: A partitioning strategy for nonuniform problems on multiprocessors. IEEE Trans. Comput. 100(5), 570–580 (1987)

    Article  Google Scholar 

  16. Farhat, C., Lesoinne, M.: Automatic partitioning of unstructured meshes for the parallel solution of problems in computational mechanics. Int. J. Numer. Meth. Eng. 36(5), 745–764 (1993)

    Article  MATH  Google Scholar 

  17. Smith, C.: The planet’s 24 largest social media sites, and where their next wave of growth will come from. Business Insider 8 (2013)

    Google Scholar 

  18. Bui, T.N., Jones, C.: A heuristic for reducing fill-in in sparse matrix factorization. Technical report, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (United States) (1993)

    Google Scholar 

  19. Cheng, C.K., Wei, Y.C., et al.: An improved two-way partitioning algorithm with stable performance [VLSI]. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 10(12), 1502–1511 (1991)

    Article  Google Scholar 

  20. Garbers, J., Promel, H.J., Steger, A.: Finding clusters in VLSI circuits. In: 1990 IEEE International Conference on Computer-Aided Design, ICCAD 1990. Digest of Technical Papers, pp. 520–523. IEEE (1990)

    Google Scholar 

  21. Hagen, L., Kahng, A.B.: A new approach to effective circuit clustering. In: 1992 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 1992. Digest of Technical Papers, pp. 422–427. IEEE (1992)

    Google Scholar 

  22. Jin, S., Zhang, J., Yu, P.S., Yang, S., Li, A.: Synergistic partitioning in multiple large scale social networks. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 281–290. IEEE (2014)

    Google Scholar 

  23. Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48(1), 96–129 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  24. Karypis, G., Kumar, V.: Parallel multilevel series k-way partitioning scheme for irregular graphs. SIAM Rev. 41(2), 278–300 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  25. Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. Studholme, C., Hill, D.L., Hawkes, D.J.: An overlap invariant entropy measure of 3d medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)

    Article  Google Scholar 

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Acknowledgments

The work described in this paper is partially supported by National Key fundamental Research and Development Program (No. 2013CB329601, No. 2013CB329606) and National Natural Science Foundation of China (No. 61502517, No. 61372191, No. 61572492). The author is grateful to the anonymous referee for a careful checking of the details and helpful comments that improved this paper.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, China

    Fenglan Li, Anming Ji, Songchang Jin, Shuqiang Yang & Qiang Liu

Authors
  1. Fenglan Li
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  2. Anming Ji
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  3. Songchang Jin
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  4. Shuqiang Yang
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  5. Qiang Liu
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Corresponding author

Correspondence to Fenglan Li .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University, Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Li, F., Ji, A., Jin, S., Yang, S., Liu, Q. (2016). Collaborative Partitioning for Multiple Social Networks with Anchor Nodes. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_27

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  • DOI: https://doi.org/10.1007/978-3-319-39937-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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