Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay

ABSTRACT

Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking.

In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.

References

1. R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1):47, 2002.
2. A. Ansari, O. Koenigsberg, and F. Stahl. Modeling multiple relationships in social networks. Journal of Marketing Research, 48(4):713--728, 2011.
3. A.-L. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509--512, 1999.
4. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. Complex networks: Structure and dynamics. Physics Reports, 424(4):175--308, 2006.
5. D. M. Boyd and N. B. Ellison. Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1):210--230, 2007.
6. R. J. Carroll and D. Ruppert. Transformation and weighting in regression, volume 30. CRC Press, 1988.
7. R. K. Chandy and G. J. Tellis. Organizing for radical product innovation: The overlooked role of willingness to cannibalize. Journal of Marketing Research, pages 474--487, 1998.
8. P. Erdõs and A. Rényi. On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci, 5:17--61, 1960.
9. G. Facchetti, G. Iacono, and C. Altafini. Computing global structural balance in large-scale signed social networks. PNAS, 108(52):20953--20958, 2011.
10. M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In SIGCOMM, 1999.
11. O. Frank and K. Nowicki. Exploratory statistical anlaysis of networks. Annals of Discrete Mathematics, 55:349--365, 1993.
12. I. Gollini and T. B. Murphy. Joint modelling of multiple network views. Journal of Computational and Graphical Statistics, pages 00--00, 2014.
13. D. Greene and P. Cunningham. Producing a unified graph representation from multiple social network views. In Web Science Conference, pages 118--121. ACM, 2013.
14. J. W. Harris and H. Stöcker. Handbook of mathematics and computational science. Springer Science & Business Media, 1998.
15. H. Hu, X. Yan, Y. Huang, J. Han, and X. J. Zhou. Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics, 21(suppl 1):i213--i221, 2005.
16. W. Kim, O.-R. Jeong, and S.-W. Lee. On social web sites. Information Systems, 35(2):215--236, 2010.
17. T. Kollmann, A. Kuckertz, and I. Kayser. Cannibalization or synergy consumers' channel selection in online-offline multichannel systems. Journal of Retailing and Consumer Services, 19(2):186--194, 2012.
18. G. Kossinets and D. J. Watts. Empirical analysis of an evolving social network. Science, 311(5757):88--90, 2006.
19. A. Kumar and H. Daumé. A co-training approach for multi-view spectral clustering. In ICML, pages 393--400, 2011.
20. A. Kumar, P. Rai, and H. Daume. Co-regularized multi-view spectral clustering. In NIPS, pages 1413--1421, 2011.
21. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW, 2010.
22. D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7):1019--1031, 2007.
23. S. Lipovetsky and M. Conklin. Finding items cannibalization and synergy by bws data. Journal of Choice Modelling, 12:1--9, 2014.
24. J. Liu, C. Wang, J. Gao, and J. Han. Multi-view clustering via joint nonnegative matrix factorization. In SDM, volume 13, pages 252--260. SIAM, 2013.
25. S. A. Marvel, J. Kleinberg, R. D. Kleinberg, and S. H. Strogatz. Continuous-time model of structural balance. PNAS, 108(5):1771--1776, 2011.
26. M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, pages 415--444, 2001.
27. P. Pattison and S. Wasserman. Logit models and logistic regressions for social networks: II. multivariate relations. British Journal of Mathematical and Statistical Psychology, 52(2):169--194, 1999.
28. J. Pei, D. Jiang, and A. Zhang. On mining cross-graph quasi-cliques. In KDD, pages 228--238. ACM, 2005.
29. D. M. Romero, B. Meeder, V. Barash, and J. M. Kleinberg. Maintaining ties on social media sites: The competing effects of balance, exchange, and betweenness. In ICWSM, 2011.
30. M. Salter-Townshend and T. H. McCormick. Latent space models for multiview network data. Technical Report 622, Department of Statistics, University of Washington, 2013.
31. F. Schneider, A. Feldmann, B. Krishnamurthy, and W. Willinger. Understanding online social network usage from a network perspective. In SIGCOMM, pages 35--48. ACM, 2009.
32. N. Shi, M. K. Lee, C. M. Cheung, and H. Chen. The continuance of online social networks: how to keep people using facebook? In System Sciences (HICSS), pages 1--10. IEEE, 2010.
33. V. Sindhwani and P. Niyogi. A co-regularized approach to semi-supervised learning with multiple views. In ICML Workshop on Learning with Multiple Views, 2005.
34. Z. Zeng, J. Wang, L. Zhou, and G. Karypis. Coherent closed quasi-clique discovery from large dense graph databases. In KDD, pages 797--802. ACM, 2006.
35. D. Zhang, F. Wang, C. Zhang, and T. Li. Multi-view local learning. In AAAI, pages 752--757, 2008.
36. D. Zhou and C. J. Burges. Spectral clustering and transductive learning with multiple views. In ICML, pages 1159--1166. ACM, 2007.

Index Terms

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing design and evaluation methods

      • Social network analysis

    • Collaborative and social computing theory, concepts and paradigms

      • Social networks

      • Social recommendation

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems

  • Information systems applications

    • Data mining

• Networks

  • Network properties

    • Network dynamics