skip to main content
10.1145/2505515.2505561acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

To stay or not to stay: modeling engagement dynamics in social graphs

Published:27 October 2013Publication History

ABSTRACT

Given a large social graph, how can we model the engagement properties of nodes? Can we quantify engagement both at node level as well as at graph level? Typically, engagement refers to the degree that an individual participates (or is encouraged to participate) in a community and is closely related to the important property of nodes' departure dynamics, i.e., the tendency of individuals to leave the community. In this paper, we build upon recent work in the field of game theory, where the behavior of individuals (nodes) is modeled by a technology adoption game. That is, the decision of a node to remain engaged in the graph is affected by the decision of its neighbors, and the "best practice" for each individual is captured by its core number - as arises from the k-core decomposition. After modeling and defining the engagement dynamics at node and graph level, we examine whether they depend on structural and topological features of the graph. We perform experiments on a multitude of real graphs, observing interesting connections with other graph characteristics, as well as a clear deviation from the corresponding behavior of random graphs. Furthermore, similar to the well known results about the robustness of real graphs under random and targeted node removals, we discuss the implications of our findings on a special case of robustness - regarding random and targeted node departures based on their engagement level.

References

  1. The DBLP Computer Science Bibliography, http://www.informatik.uni-trier.de/~ley/db/.Google ScholarGoogle Scholar
  2. R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Rev. Mod. Phys., 74:47--97, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  3. J. I. Alvarez-Hamelin, , L. Dall'Asta, A. Barrat, and A. Vespignani. k-core decomposition of internet graphs: hierarchies, self-similarity and measurement biases. NHM, 3(2):371, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  4. A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD, pages 7--15, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD, pages 44--54, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. Baeza-Yates and M. Lalmas. User engagement:the network effect matters! In CIKM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. V. Batagelj and M. Zaversnik. An o(m) algorithm for cores decomposition of networks. CoRR, 2003.Google ScholarGoogle Scholar
  8. K. Bhawalkar, J. Kleinberg, K. Lewi, T. Roughgarden, and A. Sharma. Preventing unraveling in social networks: the anchored k-core problem. In ICALP, pages 440--451. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Carmi, S. Havlin, S. Kirkpatrick, Y. Shavitt, and E. Shir. A model of internet topology using k-shell decomposition. PNAS, 104(27):11150--11154, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  10. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, NY, USA, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. F. Gleich and C. Seshadhri. Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. In KDD, pages 597--605, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Harkins. Network games with perfect complements. Technical report, University of Warwick, February 2013.Google ScholarGoogle Scholar
  13. M. Kitsak, L. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. Stanley, and H. Makse. Identification of influential spreaders in complex networks. Nature Physics, 6(11):888--893, Aug 2010.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Lattanzi and D. Sivakumar. Affiliation networks. In STOC, pages 427--434, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Leskovec, J. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrinking diameters. ACM TKDD, 1(1), 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large welldefined clusters. Internet Mathematics, 6(1):29--123, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  17. V. H. Manshadi and R. Johari. Supermodular network games. In Allerton, pages 1369--1376, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC, pages 29--42, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Pittel, J. Spencer, and N. Wormald. Sudden emergence of a giant k-core in a random graph. J. Combin. Theory Ser. B, 67(1):111--151, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In ISWC, pages 351--368, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In WWW, pages 695--704, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. B. Seidman. Network structure and minimum degree. Social Networks, 5:269--287, 1983.Google ScholarGoogle ScholarCross RefCross Ref
  23. J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg. Structural diversity in social contagion. PNAS, 109(16):5962--5966, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  24. B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In WOSN, pages 37--42, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. S. Wu, A. Das Sarma, A. Fabrikant, S. Lattanzi, and A. Tomkins. Arrival and departure dynamics in social networks. In WSDM, pages 233--242, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Y. Zhang and S. Parthasarathy. Extracting analyzing and visualizing triangle k-core motifs within networks. In ICDE, pages 1049--1060, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. To stay or not to stay: modeling engagement dynamics in social graphs

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
      October 2013
      2612 pages
      ISBN:9781450322638
      DOI:10.1145/2505515

      Copyright © 2013 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 October 2013

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CIKM '13 Paper Acceptance Rate143of848submissions,17%Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader