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Community Structure and Information Cascade in Signed Networks

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Abstract

In this paper, we study information cascade in networks with positive and negative edges. The cascade depth is correlated with community structure of signed networks where communities are defined such that positive inter-community and negative intra-community links are minimized. The cascade is initialized from a number of nodes that are selected randomly. Finally, the number of nodes that have participated in the cascade is interpreted as cascade depth; the more the number of such nodes, the more the depth of the cascade. We investigate influence of community structure (i.e., percentage of inter-community positive and intra-community negative links) on the cascade depth. We find significant influence of community structure on cascade depth in both model and real networks. Our results show that the more the intra-community negative links (i.e., the worse the community structure), the more the cascade depth.

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References

  1. Barabási, A.-L., “Network science,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 1987, 2013.

  2. Barabási A.-L.: “Scale-free networks: a decade and beyond”. Science, 325(5939), 412–413 (2009)

    Article  MathSciNet  Google Scholar 

  3. Costa L.d.F, Oliveira Jr O.N, Travieso G, Rodrigues F.A, Villas Boas P.R, Antiqueira L, Viana M.P, Correa Rocha L.E: “Analyzing and modeling real-world phenomena with complex networks: a survey of applications,”. Advances in Physics, 60(3), 329–412 (2011)

    Article  Google Scholar 

  4. Król, D., “On Modelling Social Propagation Phenomenon,” in Intelligent Information and Database Systems, 8398, pp. 227–236, Springer International Publishing, http://dx.doi.org/10.1007/978-3-319-05458-2_24, 2014.

  5. Leskovec J, Adamic L.A, Huberman B.A.: “The dynamics of viral marketing,”. ACM Transactions on the Web (TWEB), 1(1), 5 (2007)

    Article  Google Scholar 

  6. Newman M. E, Park J: “Why social networks are different from other types of networks,”. Physical Review E, 68(3), 036122 (2003)

    Article  Google Scholar 

  7. Girvan M, Newman M. E: “Community structure in social and biological networks,”. Proc. of the National Academy of Sciences, 99(12), 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Easley, D. and Kleinberg, J., Networks, crowds, and markets, 8, Cambridge Univ. Press, 2010.

  9. Coupechoux, E. and Lelarge, M., “How clustering affects epidemics in random networks,” arXiv preprint arXiv:1202.4974, 2012.

  10. Doreian P, Mrvar A: “Partitioning signed social networks,”. Social Networks, 31(1), 111 (2009)

    Article  Google Scholar 

  11. Antal T, Krapivsky P. L, Redner S: “Social balance on networks: The dynamics of friendship and enmity,”. Physica D: Nonlinear Phenomena, 224(1), 130–136 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cartwright D, Harary F: “Structural balance: a generalization of heider’s theory,”. Psychological review, 63(5), 277 (1956)

    Article  Google Scholar 

  13. Fan P, Wang H, Li P, Li W, Jiang Z: “Analysis of opinion spreading in homogeneous networks with signed relationships,”. Journal of Statistical Mechanics: Theory and Experiment, 2012(08), P08003 (2012)

    Article  Google Scholar 

  14. Kostka, J., Oswald. Y. A. and Wattenhofer, R., “Word of mouth: Rumor dissemination in social networks,” in Structural Information and Communication Complexity, pp. 185–196, Springer, 2008.

  15. Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincon, D., Sun, X., Wang, Y., Wei, W. and Yuan, Y., “Influence maximization in social networks when negative opinions may emerge and propagate,” in SDM, pp. 379–390, 2011.

  16. Li, Y., Chen, W., Wang, Y. and Zhang, Z.-L., “Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships,” in Proc. of the sixth ACM international conference on Web search and data mining, pp. 657–666, ACM, 2013.

  17. Newman M.E.: “Modularity and community structure in networks,”. Proc. of the National Academy of Sciences 103, 23, pp. 8577–582, 2006.

  18. Babaei M, Ghassemieh H, Jalili M: “Cascading failure tolerance of modular small-world networks,”. Circuits and Systems II: Express Briefs, IEEE Transactions on, 58(8), 527–531 (2011)

    Article  Google Scholar 

  19. Jalili, M., “Synchronizability of complex networks with community structure,” International Journal of Modern Physics C, 23, 04, 2012.

  20. Huang L, Park K, Lai Y.-C, Yang L, Yang K: “Abnormal synchronization in complex clustered networks,”. Physical review letters, 97(16), 164101 (2006)

    Article  Google Scholar 

  21. Javari, A. and Jalili, M., “Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links,” ACM Transaction on Intelligent Systems and Technology, 2013.

  22. Erdos P, Rényi A: “On the evolution of random graphs,”. Publ. Math. Inst. Hungar. Acad. Sci, 5, 17–61 (1960)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mahsa Shafaei & Mahdi Jalili

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  1. Mahsa Shafaei
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  2. Mahdi Jalili
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Correspondence to Mahdi Jalili.

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Shafaei, M., Jalili, M. Community Structure and Information Cascade in Signed Networks. New Gener. Comput. 32, 257–269 (2014). https://doi.org/10.1007/s00354-014-0404-7

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  • DOI: https://doi.org/10.1007/s00354-014-0404-7

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