Advertisement

Centrality in Dynamic Competition Networks

  • Anthony BonatoEmail author
  • Nicole Eikmeier
  • David F. Gleich
  • Rehan Malik
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

Competition networks are formed via adversarial interactions between actors. The Dynamic Competition Hypothesis predicts that influential actors in competition networks should have a large number of common out-neighbors with many other nodes. We empirically study this idea as a centrality score and find the measure predictive of importance in several real-world networks including food webs, conflict networks, and voting data from Survivor.

Notes

Acknowledgments

The research for this paper was supported by grants from NSERC and Ryerson University. Gleich and Eikmeier acknowledge the support of NSF Awards IIS-1546488, CCF-1909528, the NSF Center for Science of Information STC, CCF-0939370, and the Sloan Foundation.

References

  1. 1.
    Allesina, S., Pascual, M.: Googling food webs: can an eigenvector measure species’ importance for coextinctions? PLoS Comput. Biol. 59, e1000494 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baird, D., Ulanowicz, R.E.: The seasonal dynamics of the Chesapeake Bay ecosystem. Ecol. Monogr. 594, 329–364 (1989)CrossRefGoogle Scholar
  3. 3.
    Batagelj, V., Mrvar, A.: Pajek food web datasets. http://vlado.fmf.uni-lj.si/pub/networks/data/
  4. 4.
    Boginski, V., Butenko, S., Pardalos, P.M.: On structural properties of the market graph. In: Innovation in Financial and Economic Networks, Edward Elgar Publishers, pp. 29–45 (2003)Google Scholar
  5. 5.
    Bonato, A.: A Course on the Web Graph. American Mathematical Society Graduate Studies Series in Mathematics, Rhode Island (2008)CrossRefGoogle Scholar
  6. 6.
    Bonato, A., Eikmeier, N., Gleich, D.F., Malik, R.: Dynamic competition networks: detecting alliances and leaders. In: Proceedings of Algorithms and Models for the Web Graph (WAW 2018) (2018)Google Scholar
  7. 7.
    Bonato, A., Tian, A.: Complex networks and social networks. In: Kranakis, E. (ed.) Social Networks. Mathematics in Industry Series. Springer, Berlin (2011)Google Scholar
  8. 8.
    Brandes, U., Erlebach, T. (eds.): Network Analysis: Methodological Foundations. LNCS 3418. Springer, Berlin (2005) zbMATHGoogle Scholar
  9. 9.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets Reasoning about a Highly Connected World. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  10. 10.
    Gower, J.C., Warrens, M.J.: Similarity, dissimilarity, and distance, measures of, Wiley StatsRef: Statistics Reference Online (2006)Google Scholar
  11. 11.
    Guo, W., Lu, X., Donate, G.M., Johnson, S.: The spatial ecology of war and peace, Preprint (2019)Google Scholar
  12. 12.
    Heider, F.: The Psychology of Interpersonal Relations. John Wiley & Sons, Hoboken (1958)CrossRefGoogle Scholar
  13. 13.
    Leskovec, J.: The Stanford large network dataset collection. http://snap.stanford.edu/data/index.html
  14. 14.
    McDonald-Madden, E., Sabbadin, R., Game, E.T., Baxter, P.W.J., Chadès, I., Possingham, H.P.: Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016)CrossRefGoogle Scholar
  15. 15.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010) (2010)Google Scholar
  16. 16.
    Stouffer, D.B., Bascompte, J.: Compartmentalization increases food-web persistence. Proc. Nat. Acad. Sci. 1089, 3648–3652 (2011)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Tang, J., Chang, S., Aggarwal, C., Liu, H.: Negative link prediction in social media. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (WSDM 2015) (2015)Google Scholar
  19. 19.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  20. 20.
    West, D.B.: Introduction to Graph Theory, 2nd edn. Prentice Hall, New Jersey (2001)Google Scholar
  21. 21.
    Yang, S-H., Smola, A.J., Long, B., Zha, H., Chang, Y.: Friend or frenemy?: predicting signed ties in social networks. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2012) (2012)Google Scholar
  22. 22.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anthony Bonato
    • 1
    Email author
  • Nicole Eikmeier
    • 2
  • David F. Gleich
    • 3
  • Rehan Malik
    • 1
  1. 1.Ryerson UniversityTorontoCanada
  2. 2.Grinnell CollegeGrinnellUSA
  3. 3.Purdue UniversityWest LafayetteUSA

Personalised recommendations