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Viral Marketing

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Broad Learning Through Fusions

Abstract

Via the social interactions among users, information of various topics, e.g., personal interests, products, commercial services, etc. can extensively propagate throughout the networks, where lots of users can get infected and become activated. Meanwhile, the social information diffusion can bring about great commercial values, and create lots of viral marketing (Kempe et al., Maximizing the spread of influence through a social network. In KDD, 2003) opportunities. Lots of commercial companies are utilizing the information diffusion phenomenon in online social networks to promote their products or services. For instance, Apple and Huawei have been promoting their latest cell phones via Facebook and Twitter. They can provide some free cell phone samples, coupons, or even cash to certain users (with lots of followers) in Facebook, and ask them to post some good review comments or advertising photos about the cell phone. Such information will propagate to their friends and followers, who may get activated to purchase the cell phone. Commercial promotions via the online social networks have become more and more important in recent years, which even surpass the traditional print media (like newspaper, magazine, TV, and radio). At the same time, viral marketing has also become one of the most important and secure revenue sources for many online social platforms, like Facebook and Twitter.

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References

  1. L. Adamic, R. Lukose, A. Puniyani, B. Huberman, Search in power-law networks. CoRR, cs.NI/0103016 (2001)

    Google Scholar 

  2. G. Allport, L. Postman, The Psychology of Rumor (Henry Holt, New York, 1947)

    Google Scholar 

  3. V. Belak, S. Lam, C. Hayes, Towards maximising cross-community information diffusion, in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2012)

    Google Scholar 

  4. S. Bharathi, D. Kempe, M. Salek, Competitive influence maximization in social networks, in International Workshop on Web and Internet Economics (2007)

    Google Scholar 

  5. S. Borgatti, M. Everett, A graph-theoretic perspective on centrality. Soc. Netw. 28(4), 466–484 (2006)

    Article  Google Scholar 

  6. C. Borgs, J. Chayes, N. Immorlica, A. Kalai, V. Mirrokni, C. Papadimitriou, The myth of the folk theorem. Games Econ. Behav. 70, 34–43 (2010)

    Article  MathSciNet  Google Scholar 

  7. A. Borodin, Y. Filmus, J. Oren, Threshold models for competitive influence in social networks, in International Workshop on Internet and Network Economics (2010)

    Google Scholar 

  8. S. Brin, L. Page, The anatomy of a large-scale hypertextual web search engine, in Proceedings of the Seventh International Conference on World Wide Web 7 (WWW7) (1998)

    Google Scholar 

  9. X. Chen, X. Deng, S. Teng, Computing Nash equilibria: Approximation and smoothed complexity, in 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06) (2006)

    Google Scholar 

  10. X. Chen, S. Teng, P. Valiant, The approximation complexity of win-lose games, in Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’07) (2007)

    Google Scholar 

  11. W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’09) (2009)

    Google Scholar 

  12. W. Chen, C. Wang, Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’10) (2010)

    Google Scholar 

  13. W. Chen, A. Collins, R. Cummings et al., Influence maximization in social networks when negative opinions may emerge and propagate – Microsoft research, in Conference: Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011 (2011)

    Google Scholar 

  14. Y. Chu, T. Liu, On the shortest arborescence of a directed graph. Sci. Sin. 14, 1396–1400 (1965)

    MathSciNet  MATH  Google Scholar 

  15. T. Cormen, C. Stein, R. Rivest, C. Leiserson, Introduction to Algorithms, 2nd edn. (McGraw-Hill Higher Education, New York, 2001)

    MATH  Google Scholar 

  16. C. Daskalakis, P. Goldberg, C. Papadimitriou, The complexity of computing a Nash equilibrium, in Proceedings of the Thirty-Eighth Annual ACM Symposium on Theory of Computing (STOC ’06) (2006)

    Google Scholar 

  17. S. Datta, A. Majumder, N. Shrivastava, Viral marketing for multiple products, in 2010 IEEE International Conference on Data Mining (2010)

    Google Scholar 

  18. B. Doerr, M. Fouz, T. Friedrich, Why rumors spread so quickly in social networks. Commun. ACM 55, 70–75 (2012)

    Article  Google Scholar 

  19. P. Domingos, M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’01) (2001)

    Google Scholar 

  20. J. Edmonds, Optimum branchings. J. Res. Natl. Bur. Stand. 71, 233–240 (1967)

    Article  MathSciNet  Google Scholar 

  21. U. Feige. A threshold of ln n for approximating set cover. J. ACM 45, 634–652 (1998)

    Article  MathSciNet  Google Scholar 

  22. A. Goyal, W. Lu, L. Lakshmanan, Celf++: optimizing the greedy algorithm for influence maximization in social networks, in Proceedings of the 20th International Conference Companion on World Wide Web (WWW ’11) (2011)

    Google Scholar 

  23. A. Goyal, W. Lu, L. Lakshmanan, Simpath: an efficient algorithm for influence maximization under the linear threshold model, in 2011 IEEE 11th International Conference on Data Mining (2011)

    Google Scholar 

  24. H. Gui, Y. Sun, J. Han, G. Brova, Modeling topic diffusion in multi-relational bibliographic information networks, in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM ’14) (2014)

    Google Scholar 

  25. X. He, G. Song, W. Chen, Q. Jiang, Influence blocking maximization in social networks under the competitive linear threshold model, in Conference: Proceedings of SIAM International Conference on Data Mining, SDM 2012 (2012)

    Google Scholar 

  26. P. Jaccard, Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull. Soc. Vaud. Sci. Nat. 37, 547–579 (1901)

    Google Scholar 

  27. Q. Jiang, G. Song, G. Cong, Y. Wang, W. Si, K. Xie, Simulated annealing based influence maximization in social networks, in Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)

    Google Scholar 

  28. F. Jin, E. Dougherty, P. Saraf, Y. Cao, N. Ramakrishnan, Epidemiological modeling of news and rumors on twitter, in Proceedings of the 7th Workshop on Social Network Mining and Analysis (SNAKDD ’13) (2013)

    Google Scholar 

  29. D. Kempe, J. Kleinberg, É. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)

    Google Scholar 

  30. M. Kimura, K. Saito, Approximate solutions for the influence maximization problem in a social network, in Knowledge-Based Intelligent Information and Engineering Systems, ed. by B. Gabrys, R. Howlett, L. Jain (Springer, Berlin, 2006)

    Google Scholar 

  31. X. Kong, J. Zhang, P. Yu, Inferring anchor links across multiple heterogeneous social networks, in Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (CIKM ’13) (2013)

    Google Scholar 

  32. J. Kostka, Y. Oswald, R. Wattenhofer, Word of mouth: rumor dissemination in social networks, in International Colloquium on Structural Information and Communication Complexity (2008)

    Google Scholar 

  33. S. Kwon, M. Cha, K. Jung, W. Chen, Y. Wang, Prominent features of rumor propagation in online social media, in IEEE 13th International Conference on Data Mining (2013)

    Google Scholar 

  34. K. Lai, The knapsack problem and fully polynomial time approximation schemes (fptas). Technical report (2006)

    Google Scholar 

  35. T. Lappas, E. Terzi, D. Gunopulos, H. Mannila, Finding effectors in social networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’10) (2010)

    Google Scholar 

  36. J. Leskovec, L. Adamic, B. Huberman, The dynamics of viral marketing. ACM Trans. Web 1(1), 5 (2007)

    Article  Google Scholar 

  37. W. Lu, W. Chen, L. Lakshmanan, From competition to complementarity: comparative influence diffusion and maximization, in Proceedings of VLDB Endowment (2015)

    Article  Google Scholar 

  38. R. Narayanam, A. Nanavati, Viral marketing for product cross-sell through social networks, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2012) (2012)

    Google Scholar 

  39. D. Nguyen, H. Zhang, S. Das, M. Thai, T. Dinh, Least cost influence in multiplex social networks: model representation and analysis, in 2013 IEEE 13th International Conference on Data Mining (2013)

    Google Scholar 

  40. N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani, Algorithmic Game Theory (Cambridge University Press, New York, 2007)

    Book  Google Scholar 

  41. B. Prakash, J. Vreeken, C. Faloutsos, Spotting culprits in epidemics: how many and which ones? in 2012 IEEE 12th International Conference on Data Mining (2012)

    Google Scholar 

  42. M. Richardson, P. Domingos, Mining knowledge-sharing sites for viral marketing, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)

    Google Scholar 

  43. D. Shah, T. Zaman, Detecting sources of computer viruses in networks: theory and experiment, in Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’10, New York (2010), pp. 203–214. http://doi.acm.org/10.1145/1811039.1811063

  44. D. Shah, T. Zaman, Rumors in a network: who’s the culprit? IEEE Trans. Inf. Theory 57(8), 5163–5181 (2011)

    Article  MathSciNet  Google Scholar 

  45. Y. Sun, J. Han, X. Yan, P. Yu, T. Wu, Pathsim: meta path-based top-k similarity search in heterogeneous information networks, in Proceedings of VLDB Endowment (2011)

    Google Scholar 

  46. F. Yang, Y. Liu, X. Yu, M. Yang, Automatic detection of rumor on Sina Weibo, in Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics (MDS ’12) (2012)

    Google Scholar 

  47. Q. Zhan, J. Zhang, S. Wang, P. Yu, J. Xie, Influence maximization across partially aligned heterogeneous social networks, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (2015)

    Chapter  Google Scholar 

  48. Q. Zhan, J. Zhang, P. Yu, J. Xie, Viral marketing through aligned networks. Technical report (2018)

    Google Scholar 

  49. J. Zhang, P. Yu, Z. Zhou, Meta-path based multi-network collective link prediction, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’14) (2014)

    Google Scholar 

  50. J. Zhang, S. Wang, Q. Zhan, P. Yu, Intertwined viral marketing in social networks, in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)

    Google Scholar 

  51. J. Zhang, C. Aggarwal, P. Yu, Rumor initiator detection in infected signed networks, in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017)

    Google Scholar 

  52. J. Zhang, L. Cui, Y. Fu, F. Gouza, Fake news detection with deep diffusive network model. CoRR, abs/1805.08751 (2018)

    Google Scholar 

  53. B. Zong, Y. Wu, A. Singh, X. Yan, Inferring the underlying structure of information cascades, in 12th IEEE International Conference on Data Mining (ICDM 2012) (2012)

    Google Scholar 

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Zhang, J., Yu, P.S. (2019). Viral Marketing. In: Broad Learning Through Fusions. Springer, Cham. https://doi.org/10.1007/978-3-030-12528-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-12528-8_10

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