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Parallel Seed Selection for Influence Maximization Based on k-shell Decomposition

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Collaborate Computing: Networking, Applications and Worksharing (CollaborateCom 2016)

Abstract

Influence maximization is the problem of selecting a set of seeds in a social network to maximize the influence under certain diffusion model. Prior solutions, the greedy and its improvements are time-consuming. In this paper, we propose candidate shells influence maximization (CSIM) algorithm under heat diffusion model to select seeds in parallel. We employ CSIM algorithm (a modified algorithm of greedy) to coarsely estimate the influence spread to avoid massive estimation of heat diffusion process, thus can effectively improve the speed of selecting seeds. Moreover, we can select seeds from candidate shells in parallel. Specifically, First, we employ the k-shell decomposition method to divide a social network and generate the candidate shells. Further, we use the heat diffusion model to model the influence spread. Finally, we select seeds of candidate shells in parallel by using the CSIM algorithm. Experimental results show the effectiveness and feasibility of the proposed algorithm.

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References

  1. Granovetter, M.: Threshold models of collective behavior. Am. J. Soc. 83, 1420–1443 (1978)

    Article  Google Scholar 

  2. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  3. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: SIGKDD, pp. 1029–1038 (2010)

    Google Scholar 

  4. Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD, pp. 57–66 (2001)

    Google Scholar 

  5. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD, pp. 61–70 (2002)

    Google Scholar 

  6. Kempe, D., Kleinberg, J., Tardos É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)

    Google Scholar 

  7. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions—I. Math. Program. 14(1), 265–294 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  8. Leskovec, J., Krause, A., Guestrin, C., et al.: Cost-effective outbreak detection in networks. In: KDD, pp. 420–429 (2007)

    Google Scholar 

  9. Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale social networks. Data Min. Knowl. Disc. 25(3), 545–576 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Cheng, S., Shen, H., Huang, J., Zhang, G., Cheng, X.: Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: CIKM, pp. 509–518 (2013)

    Google Scholar 

  11. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD, pp. 199–208 (2009)

    Google Scholar 

  12. Chen, Y.C., Zhu, W.Y., Peng, W.C., Lee, W.C., Lee, S.Y.: CIM: community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. 5(2), 25 (2014)

    Article  Google Scholar 

  13. Horel, T., Singer, Y.: Scalable methods for adaptively seeding a social network. In: WWW, pp. 441–451 (2015)

    Google Scholar 

  14. Chen, Y.C., Peng, W.C., Lee, S.Y.: Efficient algorithms for influence maximization in social networks. Knowl. Inf. Syst. 33(3), 577–601 (2012)

    Article  Google Scholar 

  15. Ma, H., Yang, H., Lyu, M.R., King, I.: Mining social networks using heat diffusion processes for marketing candidates selection. In: CIKM, pp. 233–242 (2008)

    Google Scholar 

  16. Bollobás, B.: Graph theory and combinatorics. In: Proceedings of the Cambridge Combinatorial Conference in honor of Paul Erdös, Academic, p. 35 (1984)

    Google Scholar 

  17. Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of Internet topology using k-shell decomposition. In: PNAS, pp. 11150–11154 (2007)

    Google Scholar 

  18. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)

    Article  Google Scholar 

  19. Ren, Z.M., Liu, J.G., Shao, F., Hu, Z.L.: Guo, Q: Analysis of the spreading influence of the nodes with minimum K-shell value in complex networks. Acta. Phys. Sin 62(10), 108902-1–108902-6 (2013)

    Google Scholar 

  20. https://spark.apache.org/docs/latest/graphx-programming-guide.html

  21. Cao, J.X., Dong, D., Xu, S., Zheng, X., Liu, B., Luo, J.Z.: A k-core based algorithm for influence maximization in social networks. Chin. J. Comput. 38(2), 238–248 (2015)

    MathSciNet  Google Scholar 

  22. Song, G., Zhou, X., Wang, Y., Xie, K.: Influence maximization on large-scale mobile social network: a divide-and-conquer method. IEEE Trans. Parallel Distrib. Syst. 26(5), 1379–1392 (2015)

    Article  Google Scholar 

  23. Kim, J., Kim, S.K., Yu, H.: Scalable and parallelizable processing of influence maximization for large-scale social networks. In: ICDE, pp. 266–277 (2013)

    Google Scholar 

  24. Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)

    Article  Google Scholar 

  25. Seo, S., Yoon, E.J., Kim, J., Jin, S., Kim, J.S., Maeng, S.: Hama: an efficient matrix computation with the mapreduce framework. In: CloudCom, pp. 721–726 (2010)

    Google Scholar 

  26. Avery, C.: Giraph: Large-scale graph processing infrastruction on hadoop. In: Hadoop Summit (2011)

    Google Scholar 

  27. Low, Y., Gonzalez, J.E., Kyrola, A., Bickson, D., Guestrin, C.E., Hellerstein, J.: Graphlab: a new framework for parallel machine learning. In: UAI, p. 10, (2014)

    Google Scholar 

  28. Xin, R.S., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: a resilient distributed graph system on spark. In: GRADES, pp. 2:1–2:6 (2013)

    Google Scholar 

  29. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: ICDM, pp. 745–754 (2012)

    Google Scholar 

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 61472345, 61462056, 61402398), Natural Science Foundation of Yunnan Province (Nos. 2014FA023, 2014FA028), Program for Excellent Young Talents of Yunnan University (No. XT412003), Research Foundation of the Education Department of Yunnan Province (Nos. 2014C134Y, 2016YJS005, 2016ZZX013).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Hong Wu, Kun Yue, Yujie Wang & Weiyi Liu

  2. School of Information Engineering, Qujing Normal University, Qujing, China

    Hong Wu

  3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Xiaodong Fu

Authors
  1. Hong Wu
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  2. Kun Yue
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  3. Xiaodong Fu
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  4. Yujie Wang
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  5. Weiyi Liu
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Corresponding author

Correspondence to Kun Yue .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wu, H., Yue, K., Fu, X., Wang, Y., Liu, W. (2017). Parallel Seed Selection for Influence Maximization Based on k-shell Decomposition. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

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