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A novel approach to online social influence maximization

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Abstract

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.

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References

  • Akcora CG, Carminati B, Ferrari E (2011) Network and profile based measures for user similarities on social networks. In: IEEE international conference on information reuse and integration (IRI), pp 292–298

  • Albert R, Jeong H, Barabasi A (2000) Error and attack tolenrance of complex networks. Nature 406:378–382

    Article  Google Scholar 

  • Alizadeh F (1995) Interior point methods in semidefinite programming with applications to combinatorial optimization. SIAM J Optim 5:13–51

    Article  MathSciNet  MATH  Google Scholar 

  • Aral S (2011) Identifying social influence: a Comment on opinion leadership and social contagion in new product diffusion. Mark Sci 30(2)217–223

    Article  Google Scholar 

  • Bakshy E et al (2011) Everyones an influencer: quantifying influence on Twitter. In: WSDM, pp 65–74

  • Bhagat S, Goyal A, Lakshmanan LVS (2012) Maximizing product adoption in social networks. In: WSDM, pp 603–612

  • Bross J, Richly K, Kohnen M, Meinel C (2012) Identifying the top-dogs of the blogosphere. Soc Netw Analy Min 2(1):53–67

    Article  Google Scholar 

  • Burer S, Monteiro R (2004) Local minima and convergence in low-rank semidefinite programming. Math Progr 103(3):427–444

    Article  MathSciNet  Google Scholar 

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

  • Chen W, Yuan Y, Zhang L (2010b) Scalable influence maximization in social networks under the linear threshold model. In: ICDM, pp 88–97

  • Christianson B, Harbison WS (1996) Why isnt trust transitivie? In: International workshop on security protocols, pp 171–176

  • Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD conference on knowledge discovery and data mining, pp 57–66

  • Ferri F, Grifoni P, Guzzo T (2012) New forms of social and professional digital relationships: the case of Facebook. Soc Netw Analy Mini 2(2):121–137

    Article  Google Scholar 

  • Gimpel J, Karnes K, Mctague J, Pearson-Merkowitz S (2008) Distance-decay in the political geography of friends-and-neighbors voting. Polit Geogr 27:231–252

    Article  Google Scholar 

  • Goemans MX, Williamson DP (1995) Improved approximation algorithms for maximum cut and satisfiability using semidefinite programming. J ACM, 42(6):1145

    Article  MathSciNet  Google Scholar 

  • Goyal A, Bonchi F, Lakshmanan LVS (2010) Learning influence probabilities in social networks. In: WSDM, pp 241–250

  • Goyal A, Lu W, Lakshmanan LVS (2011) A data-based approach to social influence maximization. In: PVLDB, vol 5, no 1

  • Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: WWW, pp 403–412

  • Hang C, Wang Y, Singh M (2009) Operators for propagating trust and their evaluation in social networks. In: AAMAS, pp 1025–1032

  • Ienco D, Bonchi F, Castillo C (2010) The meme ranking problem: maximizing microblogging virality. In: SIASP workshop of ICDM, pp 328–335

  • Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Mark Sci 30(2):195–212

    Article  Google Scholar 

  • Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp 135–142

  • Jin Y, Lin CY, Matsuo Y, Ishizuka M (2012) Mining dynamic social networks from public news articles for company value prediction. Soc Netw Anal Min 2(3):217–228

    Article  Google Scholar 

  • Josang A, Pope S.: (2005) Semantic constraints for trust transitivity. In: APCCM, pp 59–68

  • Kempe D, Kleinberg JM, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD conference on knowledge discovery and data mining, pp 137–146

  • Kimura M, Saito K (2006) Tractable models for information diffusion in social networks. In: Proceedings of the 10th European conference on principles and practice of knowledge discovery in databases, pp 259–271

  • Lappas T, Terzi E, Gunopulos D, Mannila H (2010) Finding effectors in social networks. In: KDD, pp 1059–1068

  • Leskovec J, Krause A, Guestrin C, Faloutsos C, Van-Briesen J, Glance NS (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD conference on knowledge discovery and data mining, pp 420–429

  • Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5

    Article  Google Scholar 

  • Li L, Wang Y, Lim E (2009) Trust-oriented composite services selection and discovery. In: ICSOC, pp 50–67

  • Ma H, King I, Lyu MR (2007) Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, USA, pp 39–46

  • Mathioudakis M, Bonchi F, Castillo C, Gionis A, Ukkonen A (2011) Sparsification of influence networks. In: KDD, pp 529–537

  • Nail J (2004) The consumer advertising backlash. In: Forrester research and intelliseek market research report

  • Richardson M, Domingos P (2002) 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

  • Saito K et al (2008) Prediction of information diffusion probabilities for independent cascade model. In: KES, pp 67–75

  • Song X, Chi Y, Hino K, Tseng BL (2007) Information flow modeling based on diffusion rate for prediction and ranking. In: WWW, pp 191–200

  • Walter F, Battiston S, Schweitzer F (2008) A model of a trust-based recommendation system on a social network. AAMAS 16(1):57–74

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis. Cambridge university Press, Cambridge

    Book  Google Scholar 

Download references

Acknowledgments

This research work was supported in part by National Science Foundation of USA under Grants CNS 1016320 and CCF 0829993.

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Correspondence to Wen Xu.

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Xu, W., Lu, Z., Wu, W. et al. A novel approach to online social influence maximization. Soc. Netw. Anal. Min. 4, 153 (2014). https://doi.org/10.1007/s13278-014-0153-0

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  • DOI: https://doi.org/10.1007/s13278-014-0153-0

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