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Influence Maximization on Complex Networks with Intrinsic Nodal Activation

  • Arun V. SathanurEmail author
  • Mahantesh Halappanavar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)

Abstract

In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. Under such scenarios, influencer mining does not involve explicit seeding as in the case of viral marketing. Being an influencer necessitates creating content and disseminating the same to active followers who can then spread the same on the network. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarily because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. A small example is provided to illustrate how the incorporation of intrinsic and influenced activation mechanisms help us better model the influence dynamics in social networks. Following that, for a larger dataset, we compare the lists of influential users identified by the given formulation with a computationally efficient centrality metric derived from a linear probabilistic model that incorporates self activation.

Keywords

Complex networks Influence maximization Social influence Self-activation Centrality Spectral methods 

Notes

Acknowledgement

This research was supported by the High Performance Data Analytics program at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi- program national laboratory operated by Battelle Memorial Institute for the US Department of Energy under DE-AC06-76RLO1830.

References

  1. 1.
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43. ACM (2005)Google Scholar
  2. 2.
    Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincon, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SIAM Data Mining, pp. 379–390 (2011)Google Scholar
  3. 3.
    Farajtabar, M., Du, N., Gomez-Rodriguez, M., Valera, I., Zha, H., Song, L.: Shaping social activity by incentivizing users. In: Advances in Neural Information Processing Systems, pp. 2474–2482 (2014)Google Scholar
  4. 4.
    Friedkin, N.E., Johnsen, E.C.: Social influence networks and opinion change. Ad. Group Proces. 16(1), 1–29 (1999)Google Scholar
  5. 5.
    Gionis, A., Terzi, E., Tsaparas, P.: Opinion maximization in social networks. In: SIAM Data Mining Conference, pp. 387–395. SIAM (2013)Google Scholar
  6. 6.
    Halappanavar, M., Sathanur, A., Nandi, A.: Accelerating the mining of influential nodes in complex networks through community detection. In: Proceedings of the 13th ACM International Conference on Computing Frontiers, CF 2016, Como, Italy, May 16–18, 2016Google Scholar
  7. 7.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD, pp. 137–146. ACM, New York (2003)Google Scholar
  8. 8.
    Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005). doi: 10.1007/11523468_91 CrossRefGoogle Scholar
  9. 9.
    Krause, A., Golovin, D.: Submodular function maximization. Tractability Pract. Approaches Hard Probl. 3(19), 8 (2012)Google Scholar
  10. 10.
    Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 016118 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, Y., Chen, W., Wang, Y., Zhang, Z.L.: Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 657–666. ACM (2013)Google Scholar
  12. 12.
    Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: ACM SIGKDD, pp. 33–41. ACM (2012)Google Scholar
  13. 13.
    Sathanur, A.V., Jandhyala, V., Xing, C.: Physense: Scalable sociological interaction models for influence estimation on online social networks. In: IEEE International Conference on Intelligence and Security Informatics, pp. 358–363. IEEE (2013)Google Scholar
  14. 14.
    Srivastava, A., Chelmis, C., Prasanna, V.K.: Influence in social networks: A unified model? In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 451–454. IEEE (2014)Google Scholar
  15. 15.
    Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (TOIS) 28(4), 20 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Pacific Northwest National LaboratoryRichlandUSA

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