Resource and Remembering Influences on Acquaintance Networks

  • Chung-Yuan Huang
  • Chia-Ying Cheng
  • Chuen-Tsai Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)


To better reflect actual human interactions in social network models, the authors take a bottom-up, agent-based modeling and network-oriented simulation approach to analyzing acquaintance network evolution based on local interaction rules. Resources and remembering are considered in addition to common friends, meeting by chance, and leaving and arriving. Based on these factors, friendships that have been established and built up can be strengthened, weakened, or broken up. Using computer simulations, Results from a series of experiments indicate that (a) network topology statistics, especially average degree of nodes, are irrelevant to parametric distributions because they rely on average values for initial parameters; and (b) resource, remembering, and initial friendship all raise the average number of friends and lower both degree of clustering and separation. These findings indicate a strong need for a bottom-up, agent-based modeling and network-oriented simulation approach to social network research, one that stresses interactive rules and experimental simulations.


Resource remembering acquaintance networks social networks small world individual interaction 


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  1. 1.
    Davidsen, J., Ebel, H., Bornholdt, S.: Emergence of a small world from local interactions: Modeling acquaintance networks. Physical Review Letter 88(12), 128701 (2002)CrossRefGoogle Scholar
  2. 2.
    Jin, E.M., Girvan, M., Newman, M.E.J.: The structure of growing social networks. Physical Review Letters E 64, 046132 (2001)Google Scholar
  3. 3.
    Newman, M.E.J., Park, J.: Why social networks are different from other types of networks. Physical Review E 68(3), 036122 (2003)CrossRefGoogle Scholar
  4. 4.
    Davis, G., Yoo, M., Baker, W.: The small world of the American corporate elite, 1982-2001. Strategic Organization 1(3), 301–326 (2003)CrossRefGoogle Scholar
  5. 5.
    Li, X., Chen, G.: A local world evolving network model. Physical A 328, 274–286 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 100–442 (1998)CrossRefGoogle Scholar
  7. 7.
    Zanette, D.H.: Dynamics of rumor propagation on small-world networks. Physical Review E 65(4), 41908 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Watts, D.J.: Networks, dynamics, and the small-world phenomenon. American Journal of Sociology 105(2), 493–527 (1999)CrossRefGoogle Scholar
  10. 10.
    Newman, M.E.J., Watts, D.J.: Renormalization group analysis of the small-world network model. Physics Letters A 263(4-6), 346–347 (1999)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Watts, D.J., Dodds, P.S., Newman, M.E.J.: Identity and search in social networks. Science 296(5571), 1302–1305 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chung-Yuan Huang
    • 1
    • 2
  • Chia-Ying Cheng
    • 3
  • Chuen-Tsai Sun
    • 3
  1. 1.Department of Computer Science and Information Engineering 
  2. 2.Research Center for Emerging Viral InfectionsChang Gung UniversityTaoyuanTaiwan, R.O.C.
  3. 3.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan, R.O.C.

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