Analysis of Social Network Dynamics with Models from the Theory of Complex Adaptive Systems

  • Ilias Lymperopoulos
  • George Lekakos
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 399)

Abstract

The understanding and modeling of social dynamics in a complex and unpredictable world, emerges as a research target of particular importance. Success in this direction can yield valuable knowledge as to how social phenomena form and evolve in varying socioeconomic contexts comprising economic crises, societal disasters, cultural differences and security threats among others. The study of social dynamics occurring in the aforementioned contexts with the methodological tools originating from the complexity theory, is the research approach we propose in this paper. Furthermore, considering the fact that online social media serve as platforms of individual expression and public dialogue, we anticipate that their study as complex adaptive systems, will significantly contribute to understanding, predicting and monitoring social phenomena taking place on both online and offline social networks.

Keywords

Social dynamics social network analysis complex adaptive systems online social networks online social media offline social networks complex networks complexity theory scale free networks statistical physics complex adaptive systems models 

References

  1. 1.
    Moreno, J.L.: Sociogram and sociomatrix. Sociometry 9, 348–349 (1946)CrossRefGoogle Scholar
  2. 2.
    Wasserman, S., Faust, K.: Social network analysis: Methods and applications. Cambridge University Press (1994)Google Scholar
  3. 3.
    Wasserman, S., Galaskiewicz, J.: Advances in social network analysis: Research in the social and behavioral sciences. Sage Publications, Incorporated (1994)Google Scholar
  4. 4.
    Butts, C.T.: Social network analysis: A methodological introduction. Asian Journal of Social Psychology 11, 13–41 (2008)CrossRefGoogle Scholar
  5. 5.
    Bavelas, A., Barrett, D.: An experimental approach to organizational communication. American Management Association (1951)Google Scholar
  6. 6.
    Brass, D.J.: Power in organizations: A social network perspective. Research in Politics and Society 4, 295–323 (1992)Google Scholar
  7. 7.
    Burt, R.S.: V The Social Structure of Competition (1992)Google Scholar
  8. 8.
    Willer, D.: Network exchange theory. Praeger Publishers (1999)Google Scholar
  9. 9.
    Knoke, D., Kuklinski, J.H.: Network analysis: basic concepts. In: Thompson, G., et al. (organizadores) Markets, Hierarchies and Networks, pp. 173–182. Sage Publications, London (1991)Google Scholar
  10. 10.
    Burt, R.S.: Toward a structural theory of action: network models of social Structure, Perception, and Action (1982)Google Scholar
  11. 11.
    Scott, J.: Social network analysis: A handbook. Sage Publications Limited (2000)Google Scholar
  12. 12.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Hui, C., Goldberg, M., Magdon-Ismail, M., Wallace, W.A.: Simulating the diffu-sion of information: An agent-based modeling approach. International Journal of Agent Technologies and Systems (IJATS) 2, 31–46 (2010)CrossRefGoogle Scholar
  14. 14.
    Scott, J.: Social network analysis: developments, advances, and prospects. Social Network Analysis and Mining 1, 21–26 (2011)CrossRefGoogle Scholar
  15. 15.
    Wasserman, S., Pattison, P.: Logit models and logistic regressions for social networks: I. An Introduction to Markov Graphs and p. Psychometrika 61, 401–425 (1996)MathSciNetMATHGoogle Scholar
  16. 16.
    Pattison, P., Wasserman, S.: Logit models and logistic regressions for social networks: II. Multivariate Relations. British Journal of Mathematical and Statistical Psychology 52, 169–193 (1999)CrossRefGoogle Scholar
  17. 17.
    Caldarelli, G., Garlaschelli, D.: Self-organization and complex networks. Adaptive Networks, 107–135 (2009)Google Scholar
  18. 18.
    Watts, D., Strogatz, S.: The small world problem. Collective Dynamics of Small-World Networks 393, 440–442 (1998)Google Scholar
  19. 19.
    Watts, D.J.: Networks, dynamics, and the small-world phenomenon 1. American Journal of Sociology 105, 493–527 (1999)CrossRefGoogle Scholar
  20. 20.
    Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47 (2002)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Stauffer, D., Aharony, A.: Introduction to percolation theory. CRC (1994)Google Scholar
  23. 23.
    Newman, M.E.J., Watts, D.J.: Scaling and percolation in the small-world network model. Physical Review E 60, 7332 (1999)CrossRefGoogle Scholar
  24. 24.
    Pastor-Satorras, R., Vespignani, A.: Epidemics and immunization in scale-free networks. arXiv preprint cond-mat/0205260 (2002)Google Scholar
  25. 25.
    Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics in finite size scale-free networks. Physical Review E 65, 035108 (2002)CrossRefGoogle Scholar
  26. 26.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Advances in Physics 51, 1079–1187 (2002)CrossRefGoogle Scholar
  27. 27.
    Caldarelli, G., Vespignani, A.: Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science (Complex Systems and Interdisciplinary Science) (2007)Google Scholar
  28. 28.
    Rohlf, T., Bornholdt, S.: Self-organized criticality and adaptation in discrete dynamical networks. Adaptive Networks, 73–106 (2009)Google Scholar
  29. 29.
    Murray, G.M.: The quark and the jaguar: Adventures in the simple and the complex. Published by Little, Brown and Company (UK) Limited, London (1994)MATHGoogle Scholar
  30. 30.
    Gross, T., Blasius, B.: Adaptive coevolutionary networks: a review. Journal of the Royal Society Interface 5, 259–271 (2008)CrossRefGoogle Scholar
  31. 31.
    Miller, J.H., Page, S.E.: Complex adaptive systems: An introduction to computa-tional models of social life. Princeton University Press (2007)Google Scholar
  32. 32.
    Benczik, I.J., Benczik, S.Z., Schmittmann, B., Zia, R.K.P.: Opinion dynamics on an adaptive random network. Physical Review E 79, 046104 (2009)CrossRefGoogle Scholar
  33. 33.
    Traulsen, A., Santos, F., Pacheco, J.: Evolutionary games in self-organizing populations. Adaptive Networks, 253–267 (2009)Google Scholar
  34. 34.
    Hebb, D.O.: The organization of behavior. Wiely, New York (1949, 2002)Google Scholar
  35. 35.
    Do, A.L., Gross, T.: Contact processes and moment closure on adaptive networks. Adaptive Networks, 191–208 (2009)Google Scholar
  36. 36.
    Holme, P., Newman, M.E.J.: Nonequilibrium phase transition in the coevolution of networks and opinions. Physical Review E 74, 056108 (2006)CrossRefGoogle Scholar
  37. 37.
    Kauffman, S.A.: The origins of order: Self-organization and selection in evolution. Oxford University Press, USA (1993)Google Scholar
  38. 38.
    Shaw, L., Schwartz, I.: Noise induced dynamics in adaptive networks with applications to epidemiology. Adaptive Networks, 209–227 (2009)Google Scholar
  39. 39.
    Barabási, B.Y.A.L., Bonabeau, E.: Scale-Free. Scientific American (2003)Google Scholar
  40. 40.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. ACM SIGCOMM Computer Communication Review, 251–262 (1999)Google Scholar
  41. 41.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)Google Scholar
  42. 42.
    Catanese, S., Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Extraction and analysis of facebook friendship relations. Computational Social Networks, 291–324 (2012)Google Scholar
  43. 43.
    Ghoshal, G.: Structural and dynamical properties of complex networks (2009), http://141.213.232.243/handle/2027.42/64757
  44. 44.
    Palla, G., Pollner, P., Barabási, A.L., Vicsek, T.: Social group dynamics in networks. Adaptive Networks, 11–38 (2009)Google Scholar
  45. 45.
    Gross, T., Sayama, H.: Adaptive Networks, pp. 1–8 (2009)Google Scholar
  46. 46.
    Coleman, J., Katz, E., Menzel, H.: The diffusion of an innovation among physicians, Sociometry 253–270 (1957)Google Scholar
  47. 47.
    Valente, T.W., Davis, R.L.: Accelerating the diffusion of innovations using opinion leaders. The Annals of the American Academy of Political and Social Science 566, 55–67 (1999)CrossRefGoogle Scholar
  48. 48.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Physical Review Letters 86, 3200–3203 (2001)CrossRefGoogle Scholar
  49. 49.
    Tomita, K., Kurokawa, H., Murata, S.: Graph-rewriting automata as a natural extension of cellular automata. Adaptive Networks, 291–309 (2009)Google Scholar
  50. 50.
    Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 623–638 (2012)Google Scholar
  51. 51.
    Granovetter, M., Soong, R.: Threshold models of diffusion and collective behavior. Journal of Mathematical Sociology 9, 165–179 (1983)MATHCrossRefGoogle Scholar
  52. 52.
    Lopez-Pintado, D., Watts, D.J.: Social influence, binary decisions and collective dynamics. Rationality and Society 20, 399–443 (2008)CrossRefGoogle Scholar
  53. 53.
    Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. Journal of Consumer Research 34, 441–458 (2007)CrossRefGoogle Scholar
  54. 54.
    Young, K.S.: Internet addiction: The emergence of a new clinical disorder. Cyber Psychology & Behavior 1, 237–244 (1998)CrossRefGoogle Scholar
  55. 55.
    Sayama, H., Laramee, C.: Generative network automata: A generalized frame-work for modeling adaptive network dynamics using graph rewritings. Adaptive Networks, 311–332 (2009)Google Scholar
  56. 56.
    Demirel, G., Prizak, R., Reddy, P.N., Gross, T.: Opinion formation and cyclic dominance in adaptive networks. arXiv preprint arXiv:1011.1124 (2010)Google Scholar
  57. 57.
    Axelrod, R.: The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University Press (1997)Google Scholar
  58. 58.
    Helbing, D., Balietti, S.: How to do agent-based simulations in the future: From modeling social mechanisms to emergent phenomena and interactive systems design. Tech. Rep. 11-06-024, Santa Fe Institute, NM, USA (June 2011), santa Fe Working Paper (2011)Google Scholar
  59. 59.
    Pastor-Satorras, R., Rubí, M., Diaz-Guilera, A.: Statistical mechanics of complex networks. Springer (2003)Google Scholar
  60. 60.
    Newman, M., Barabasi, A.L., Watts, D.J.: The structure and dynamics of networks. Princeton University Press (2011)Google Scholar
  61. 61.
    Hunt, A., Ewing, R.: Percolation Theory: Topology and Structure. Percolation Theory for Flow in Porous Media, 1–36 (2009)Google Scholar
  62. 62.
    Erez, T., Moldovan, S., Solomon, S.: Social Anti-Percolation and Negative Word of Mouth. arXiv preprint cond-mat/0406695 (2004)Google Scholar
  63. 63.
    Mort, J.: Perspective: the applicability of percolation theory to innovation. Journal of Product Innovation Management 8, 32–38 (1991)CrossRefGoogle Scholar
  64. 64.
    Solomon, S., Weisbuch, G., de Arcangelis, L., Jan, N., Stauffer, D.: Social percolation models. Physica A: Statistical Mechanics and its Applications 277, 239–247 (2000)CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2013

Authors and Affiliations

  • Ilias Lymperopoulos
    • 1
  • George Lekakos
    • 1
  1. 1.Department of Management Science and TechnologyAthens University of Economics and BusinessAthensGreece

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