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Link Recommendation for Social Influence Maximization

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Biologically Inspired Techniques in Many Criteria Decision Making

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 271))

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Abstract

In social network analysis, the influence maximization problem recognizes a small set of seed nodes that effectively maximizes the aggregated influence under a cascading propagation model. The approach has vast implications in viral marketing, government decision making, epidemic control, and many more. In the last decades, researchers developed many strategies that effectively identify the seed nodes. Although, the seed identification process is an NP-hard problem. Sometimes, due to the network structure, the best-known seeds are incapable of propagating influence throughout the network. We observed that a tiny structural modification by adding a few links sometimes increases the aggregated influence notably. From the literature, we have observed that no prior work exists in this regard. In this paper, first, we have applied multi-objective optimization for identifying initial seeds considering aggregated influence and timestep to achieve the coverage as objectives. Then for suitable non-dominated seeds, the proposed approach computed the minimum number of required missing links against every uninfluenced node to sustain the propagation. Finally, a set of statistical techniques and locally globally tuned biogeography-based optimization are used to identify end vertices for the links recommendation. The recommended links connect non-influenced components to the influenced component and allow further influence propagation.

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References

  1. Li, F., Du, T.C.: The effectiveness of word of mouth in offline and online social networks. Expert Syst. Appl. 88, 338–351 (2017)

    Article  Google Scholar 

  2. Hsiao, J.P.H., Jaw, C., Huan, T.C.: Information diffusion and new product consumption: a bass model application to tourism facility management. J. Bus. Res. 62(7), 690–697 (2009)

    Article  Google Scholar 

  3. Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Rec. 42(2), 17–28 (2013)

    Article  Google Scholar 

  4. Hao, L., Yang, L.Z., Gao, J.M.: The application of information diffusion technique in probabilistic analysis to grassland biological disasters risk. Ecol. Model. 272, 264–270 (2014)

    Article  Google Scholar 

  5. De, S.S., Dehuri, S.: Multi-objective biogeography-based optimization for influence maximization-cost minimization in social networks. In: International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making, pp. 11–34. Springer (2019)

    Google Scholar 

  6. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International Conference on Parallel Problem Solving from Nature, pp. 849–858. Springer (2000)

    Google Scholar 

  7. Giri, P.K., De, S.S., Dehuri, S.: A novel locally and globally tuned biogeography-based optimization algorithm. In: Soft Computing: Theories and Applications, pp. 635–646. Springer (2018)

    Google Scholar 

  8. Giri, P.K., De, S.S., Dehuri, S.: Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm. J. King Saud Univ.-Comput. Inf. Sci. 33(4), 453–467 (2021)

    Google Scholar 

  9. Ritzer, G., et al.: The Blackwell Encyclopedia of Sociology, vol. 1479. Blackwell Publishing, New York, NY (2007)

    Book  Google Scholar 

  10. Ryan, B., Gross, N.: Acceptance and diffusion of hybrid corn seed in two Iowa communities. Iowa Agric. Home Econ. Exp. Stn. Res. Bull. 29(372), 1 (1950)

    Google Scholar 

  11. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  12. Iacobucci, D.: Network models of the diffusion of innovations. J. Mark. 60(3), 134 (1996)

    Google Scholar 

  13. Valente, T.W.: Network models and methods for studying the diffusion of innovations. In: Models and Methods in Social Network Analysis, vol. 28, pp. 98–116 (2005)

    Google Scholar 

  14. Kimura, M., Saito, K.: Tractable models for information diffusion in social networks. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 259–271. Springer (2006)

    Google Scholar 

  15. Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: International Colloquium on Automata, Languages, and Programming, pp. 1127–1138. Springer (2005)

    Google Scholar 

  16. Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. 30(10), 1852–1872 (2018)

    Article  Google Scholar 

  17. Fonseca, C.M., Fleming, P.J., et al.: Genetic algorithms for multiobjective optimization: formulation discussion and generalization. In: ICGA, vol. 93, pp. 416–423. Citeseer (1993)

    Google Scholar 

  18. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: improving the strength pareto evolutionary algorithm. TIK-Report 103 (2001)

    Google Scholar 

  19. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  20. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  21. Coello, C.A.C.C.: A short tutorial on evolutionary multiobjective optimization. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 21–40. Springer (2001)

    Google Scholar 

  22. Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on evolutionary multiobjective optimization. In: Metaheuristics for Multiobjective Optimisation, pp. 3–37 (2004)

    Google Scholar 

  23. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)

    Google Scholar 

  24. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)

    Google Scholar 

  25. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)

    Google Scholar 

  26. Gong, M., Yan, J., Shen, B., Ma, L., Cai, Q.: Influence maximization in social networks based on discrete particle swarm optimization. Inf. Sci. 367, 600–614 (2016)

    Article  Google Scholar 

  27. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)

    Google Scholar 

  28. Zhou, C., Zhang, P., Guo, J., Zhu, X., Guo, L.: UBLF: an upper bound based approach to discover influential nodes in social networks. In: 2013 IEEE 13th International Conference on Data Mining, pp. 907–916. IEEE (2013)

    Google Scholar 

  29. Zhou, C., Zhang, P., Guo, J., Guo, L.: An upper bound based greedy algorithm for mining top-k influential nodes in social networks. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 421–422 (2014)

    Google Scholar 

  30. Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048 (2010)

    Google Scholar 

  31. Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. Knowl. Inf. Syst. 37(3), 555–584 (2013)

    Article  Google Scholar 

  32. Guo, J., Zhang, P., Zhou, C., Cao, Y., Guo, L.: Item-based top-k influential user discovery in social networks. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 780–787. IEEE (2013)

    Google Scholar 

  33. Goyal, A., Bonchi, F., Lakshmanan, L.V.: A data-based approach to social influence maximization. arXiv preprint arXiv:1109.6886 (2011)

  34. Zhou, C., Zhang, P., Zang, W., Guo, L.: Maximizing the cumulative influence through a social network when repeat activation exists. Procedia Comput. Sci. 29, 422–431 (2014)

    Article  Google Scholar 

  35. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250 (2010)

    Google Scholar 

  36. Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 67–75. Springer (2008)

    Google Scholar 

  37. Yang, J., Liu, J.: Influence maximization-cost minimization in social networks based on a multiobjective discrete particle swarm optimization algorithm. IEEE Access 6, 2320–2329 (2017)

    Article  Google Scholar 

  38. Lu, Z., Zhang, W., Wu, W., Kim, J., Fu, B.: The complexity of influence maximization problem in the deterministic linear threshold model. J. Comb. Optim. 24(3), 374–378 (2012)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. S.N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake City, Kolkata, West Bengal, 700106, India

    Sagar S. De

  2. Department of Computer Science, College of Engineering Bhubaneswar, Patia, Bhubaneswar, Odisha, 751024, India

    Parimal Kumar Giri

  3. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India

    Satchidananda Dehuri

Authors
  1. Sagar S. De
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  2. Parimal Kumar Giri
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  3. Satchidananda Dehuri
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Editor information

Editors and Affiliations

  1. Fakir Mohan University, Balasore, Odisha, India

    Satchidananda Dehuri

  2. KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  3. KIIT Deemed to be University, Bhubaneswar, India

    Pradeep Kumar Mallick

  4. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

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De, S.S., Giri, P.K., Dehuri, S. (2022). Link Recommendation for Social Influence Maximization. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_7

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