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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, J., Yu, P.S.: MCD: Mutual clustering across multiple heterogeneous networks. In: IEEE BigData Congress (2015)
Zhang, J., Yu, P.S.: Mutual community detection across multiple partially aligned social networks. arXiv preprint arXiv:1506.05529 (2015)
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)
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)
Gong, N.: Using map-reduce for large scale analysis of graph-based data (2011)
Jin, S., Yu, P.S., Li, S., Yang, S.: A parallel community structure mining method in big social networks. Math. Prob. Eng. 2015 (2015)
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)
Golberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)
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)
Hendrickson, B., Leland, R.: A multilevel algorithm for partitioning graphs. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, p. 28. ACM (1995)
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)
Karypis, G., Kumar, V.: Multilevel graph partitioning schemes. In: ICPP (3), pp. 113–122 (1995)
Berger, M.J., Bokhari, S.H.: A partitioning strategy for nonuniform problems on multiprocessors. IEEE Trans. Comput. 100(5), 570–580 (1987)
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)
Smith, C.: The planet’s 24 largest social media sites, and where their next wave of growth will come from. Business Insider 8 (2013)
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)
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)
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)
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)
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)
Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48(1), 96–129 (1998)
Karypis, G., Kumar, V.: Parallel multilevel series k-way partitioning scheme for irregular graphs. SIAM Rev. 41(2), 278–300 (1999)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-39937-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39936-2
Online ISBN: 978-3-319-39937-9
eBook Packages: Computer ScienceComputer Science (R0)