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A Clustering Coefficient Network Formation Game

  • Conference paper
Algorithmic Game Theory (SAGT 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6982))

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Abstract

Social and other networks have been shown empirically to exhibit high edge clustering — that is, the density of local neighborhoods, as measured by the clustering coefficient, is often much larger than the overall edge density of the network. In social networks, a desire for tight-knit circles of friendships — the colloquial “social clique” — is often cited as the primary driver of such structure.

We introduce and analyze a new network formation game in which rational players must balance edge purchases with a desire to maximize their own clustering coefficient. Our results include the following:

  • Construction of a number of specific families of equilibrium networks, including ones showing that equilibria can have rather general binary tree-like structure, including highly asymmetric binary trees. This is in contrast to other network formation games that yield only symmetric equilibrium networks. Our equilibria also include ones with large or small diameter, and ones with wide variance of degrees.

  • A general characterization of (non-degenerate) equilibrium networks, showing that such networks are always sparse and paid for by low-degree vertices, whereas high-degree “free riders” always have low utility.

  • A proof that for edge cost α ≥ 1/2 the Price of Anarchy grows linearly with the population size n while for edge cost α less than 1/2, the Price of Anarchy of the formation game is bounded by a constant depending only on α, and independent of n. Moreover, an explicit upper bound is constructed when the edge cost is a ”simple” rational (small numerator) less than 1/2.

  • A proof that for edge cost α less than 1/2 the average vertex clustering coefficient grows at least as fast as a function depending only on α, while the overall edge density goes to zero at a rate inversely proportional to the number of vertices in the network.

  • Results establishing the intractability of even weakly approximating best response computations.

Several of our results hold even for weaker notions of equilibrium, such as those based on link stability.

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References

  1. Albers, S., Eilts, S., Even-Dar, E., Mansour, Y., Roditty, L.: On nash equilibria for a network creation game. In: SODA, pp. 89–98 (2006)

    Google Scholar 

  2. Alon, N., Demaine, E.D., Hajiaghayi, M., Leighton, T.: Basic network creation games. In: SPAA, pp. 106–113 (2010)

    Google Scholar 

  3. Bala, V., Goyal, S.: A noncooperative model of network formation. Econometrica 68(5), 1181–1230 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  4. Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bollobás, B., Riordan, O., Spencer, J., Tusnády, G.: The degree sequence of a scale-free random graph process. Random Struct. Algorithms 18(3), 279–290 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  6. Borgs, C., Chayes, J.T., Ding, J., Lucier, B.: The hitchhiker’s guide to affiliation networks: A game-theoretic approach. In: ICS (2011)

    Google Scholar 

  7. Brautbar, M., Kearns, M.: A clustering coefficient network formation game, extended version, http://arxiv.org/abs/1010.1561

  8. Easley, D., Kleinberg, J.: Networks Crowds and Markets: Reasoning about a Highly Connected World. Cambridge University Press, Cambridge (2010)

    Book  MATH  Google Scholar 

  9. Even-Dar, E., Kearns, M., Suri, S.: A network formation game for bipartite exchange economies. In: SODA, pp. 697–706 (2007)

    Google Scholar 

  10. Even-Dar, E., Kearns, M.: A small world threshold for economic network formation. In: NIPS, pp. 385–392 (2006)

    Google Scholar 

  11. Fabrikant, A., Luthra, A., Maneva, E.N., Papadimitriou, C.H., Shenker, S.: On a network creation game. In: PODC, pp. 347–351 (2003)

    Google Scholar 

  12. Heider, F.: The Psychology of Interpersonal Relations. John Wiley & Sons, Chichester (1958)

    Book  Google Scholar 

  13. Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2008)

    MATH  Google Scholar 

  14. Jackson, M.O., Wolinsky, A.: A strategic model of social and economic networks. J. of Economic Theory 71, 44–74 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  15. Johnson, C., Gilles, R.P.: Spatial social networks. Review of Economic Design 5, 273–299 (2000)

    Article  Google Scholar 

  16. Koutsoupias, E., Papadimitriou, C.H.: Worst-case equilibria. In: Meinel, C., Tison, S. (eds.) STACS 1999. LNCS, vol. 1563, pp. 404–413. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  17. Lattanzi, S., Sivakumar, D.: Affiliation networks. In: STOC, pp. 427–434 (2009)

    Google Scholar 

  18. Newman, M., Barabasi, A.L., Watts, D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006)

    MATH  Google Scholar 

  19. Watts, D.J.: Small worlds. Princeton University Press, Princeton (1999)

    Google Scholar 

  20. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  MATH  Google Scholar 

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Brautbar, M., Kearns, M. (2011). A Clustering Coefficient Network Formation Game. In: Persiano, G. (eds) Algorithmic Game Theory. SAGT 2011. Lecture Notes in Computer Science, vol 6982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24829-0_21

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  • DOI: https://doi.org/10.1007/978-3-642-24829-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24828-3

  • Online ISBN: 978-3-642-24829-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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