Modeling direct and indirect influence across heterogeneous social networks

ABSTRACT

Real-world diffusion phenomena are governed by collective behaviors of individuals, and their underlying connections are not limited to single social networks but are extended to globally interconnected heterogeneous social networks. Different levels of heterogeneity of networks in such global diffusion may also reflect different diffusion processes. In this regard, we focus on uncovering mechanisms of information diffusion across different types of social networks by considering hidden interaction patterns between them. For this study, we propose dual representations of heterogeneous social networks in terms of direct and indirect influence at a macro level. Accordingly, we propose two macro-level diffusion models by extending the Bass model with a probabilistic approach. By conducting experiments on both synthetic and real datasets, we show the feasibility of the proposed models. We find that real-world news diffusion in social media can be better explained by direct than indirect diffusion between different types of social media, such as News, social networking sites (SNS), and Blog media. In addition, we investigate different diffusion patterns across topics. The topics of Politics and Disasters tend to exhibit concurrent and synchronous diffusion by direct influence across social media, leading to high relative entropy of diverse media participation. The Arts and Sports topics show strong interactions within homogeneous networks, while interactions with other social networks are unbalanced and relatively weak, which likely drives lower relative entropy. We expect that the proposed models can provide a way of interpreting strength, directionality, and direct/indirectness of influence between heterogeneous social networks at a macro level.

References

1. ICWSM'11 Dataset. http://www.icwsm.org/data/.
2. Wikipedia Current Events in January, 2011. http://en.wikipedia.org/wiki/January_2011.
3. E. Adar and L. Adamic. Tracking information epidemics in blogspace. In WI, pages 207--214, 2005.
4. A. Barrat, M. Barthlemy, and A. Vespignani. Dynamical processes on complex networks. Cambridge University Press, 2008.
5. F. Bass. A new product growth for model consumer durables. Management Science, 15(5):215--227, 1969.
6. F. Bass. Comments on "a new product growth for model consumer durables": The Bass model. Management Science, pages 1833--1840, 2004.
7. F. Bass, T. Krishnan, and D. Jain. Why the Bass model fits without decision variables. Marketing Science, pages 203--223, 1994.
8. M. Cha, J. Pérez, and H. Haddadi. Flash floods and ripples: The spread of media content through the blogosphere. In ICWSM, 2009.
9. A. Clauset, C. R. Shalizi, and M. E. Newman. Power-law distributions in empirical data. SIAM review, 51(4):661--703, 2009.
10. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In WWW, pages 491--501. ACM, 2004.
11. A. Kaplan and M. Haenlein. Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 53(1):59--68, 2010.
12. M. Kim, L. Xie, and P. Christen. Event diffusion patterns in social media. In ICWSM. AAAI, 2012.
13. V. Kumar and T. Krishnan. Multinational diffusion models: An alternative framework. Marketing Science, pages 318--330, 2002.
14. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD, pages 497--506. ACM, 2009.
15. J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In SDM, 2007.
16. M. Luu, E. Lim, T. Hoang, and F. Chua. Modeling diffusion in social networks using network properties. In ICWSM, 2012.
17. V. Mahajan, E. Muller, and F. Bass. Diffusion of new products: Empirical generalizations and managerial uses. Marketing Science, 14(3):G79--G88, 1995.
18. S. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In KDD, pages 33--41. ACM, 2012.
19. M. Newman. Networks: an introduction. Oxford University Press, 2010.
20. W. Putsis Jr, S. Balasubramanian, E. Kaplan, and S. Sen. Mixing behavior in cross-country diffusion. Marketing Science, pages 354--369, 1997.
21. D. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics. In WWW, pages 695--704. ACM, 2011.
22. F. Stutzman, J. Vitak, N. B. Ellison, R. Gray, and C. Lampe. Privacy in interaction: Exploring disclosure and social capital in facebook. In ICWSM, 2012.
23. J. H. Zar. Biostatistical Analysis. Prentice Hall, 2010.

Index Terms

• Information systems

  • Information storage systems

    • Storage architectures

      • Storage network architectures