Social networks analysis (that is, the study of interactions among social actors from a structural viewpoint) has a long tradition covering several decades [1, 2, 3]. This sort of study has usually been performed over small social networks, and the limitation of size has conditioned the visibility of complexity [4, 5]. However, the situation has changed significantly in recent times due to basically two reasons. First, there is an increasing availability of larger social datasets (obtained in most cases from information and communication technologies). Secondly, a large number of physicists and other scholars from complexity science have started to take active interest in the field. New perspectives and tools have been provided by these ‘newcomers’, which in combination with the expertise and knowledge accumulated by ‘classical’ social network analysts, has formed the basis of a multidisciplinary field suitably termed the science of networks [6, 7].
This research has led to the formal definition of the complexity exhibited by social networks against the following simple ‘check list’ [5].
-
1.
The network must consist of a large number of nodes showing substantial heterogeneity. Here we understand heterogeneity to mean diversity of degree.
-
2.
Its structure has to present an ‘intricate architecture’, that is, a topology that cannot be expressed in terms of simple patterns (like ‘regular’ or ‘completely random’) but must include several degrees of freedom.
-
3.
This topological complexity is translated into the global system behavior in the form of ‘emergent phenomena’, i.e. even simple local interaction rules lead to a performance of the whole system that is richer than the sum of local effects.
-
4.
This influence of local feedbacks over the macroscopical behavior can be manifested, in particular, as nonlinearities in the operation of the processes that shape the network itself (i.e. sudden emergencies of determined structural features are observed when a certain external parameter exceeds a certain threshold value).
Regarding the fulfillment of this list of requirements by social networks, Vega-Redondo refers to the results of previous studies about social structure to confirm that social networks satisfy the first two. Following the same reasoning, we notice that the other two requirements (covering dynamic aspects) are repeatedly recognized in social phenomena, for instance, collective behavior and social mobilization [8, 9] (third point), or the emergence of hierarchical social structures from interactions at an individual level [10, 11] (fourth point).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Freeman, L.C.: The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press, Vancouver (BC Canada) (2004).
Scott, J.: Social Network Analysis: A Handbook. SAGE Publications, London (2000).
Wasserman, S., Faust, K.: Social Networks Analysis: Methods and Applications. Cambridge University Press, New York (1994).
Holme, P., Edling, C.R., Liljeros, F.: Structure and time evolution of an Internet dating community. Social Networks 26, 155–174 (2004).
Vega-Redondo, F.: Complex Social Networks. Cambridge University Press, New York (2007).
Watts, D.J.: Six Degrees: The Science of a Connected Age. W. W. Norton & Company Inc., New York (2003).
Barabási, A.-L.: Linked: The New Science of Networks. Perseus Publishing, Cambridge (USA) (2002).
Coleman, J.: Foundations of Social Theory. Harvard University Press, Cambridge, MA (1990).
Gould, R.V.: Collective action and network structure. American Sociological Review 58 (2), 182–196 (1993).
Gould, R.V.: The origins of status hierarchies: A formal theory and empirical test. American Journal of Sociology 107 (5), 114378 (2002).
Epstein, J.M.: Generating classes without conquest. In: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton, NJ (2007).
Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Reviews of Modern Physics (Accepted) 348 (2008).
Rogers, E.M.: Diffusion of Innovations (5th ed.). Free Press, New York (2003).
Valente, T.W.: Models and methods for innovation diffusion. In: Carrington, P., Scott, J., Wasserman, S. (ed) Models and Methods in Social Network Analysis. Cambridge University Press, New York (2005).
Abramson, G., Kuperman, M.: Social games in a social network. Phys. Rev. E 63, 030901 (2001).
Duran, O., Mulet, R.: Evolutionary prisoners dilemma in random graphs. Physica D 208 (3–4), 257–265 (2005).
Santos, F.C., Pacheco, J.M., Lenaerts, T.: Evolutionary dynamics of social dilemmas in structured heterogeneous populations. Proc. Natl. Acad. Sci. 103, 3490–3494 (2006).
Lozano, S., Arenas, A., Sanchez, A.: Mesoscopic structure conditions the emergence of cooperation on social networks. PLoS ONE 3(4): e1892 doi: 10.1371/journal.pone.0001892 (2008).
Castellano, C., Loreto, V., Barrat, A., Cecconi, F., Parisi, D.: Comparison of voter and Glauber ordering dynamics on networks. Phys. Rev. E 71 (6), 066107 (2005).
Sood, V., Redner, S.: Voter model on heterogeneous graphs. Phys. Rev. Lett. 94 (17), 178701 (2005).
Castellano, C., Vilone, D., Vespignani, A.: Incomplete ordering of the voter model on small-world networks. Europhys. Lett. 63 (1), 153158 (2003).
Szabó, G., Fáth, G.: Evolutionary games on graphs. Phys. Rep. 446 (4–6), 97–216 (2007).
Stauffer, D.: Sociophysics Simulations II: Opinion Dynamics. arXiv:physics/0503115v1 [physics.soc-ph] (2005).
Bjelland, J., Canright, G., Engø-Monsen, K., Remple, V.P.: Topographic spreading analysis of an empirical sex workers network. In: (ed). Springer, Berlin (2008).
Doreian, P., Stokman, F.N. (ed): Evolution of Social Networks. Routledge, London (1997).
Borgatti, S.P.: The State of Organizational Social Network Research Today. Dept. of Organization Studies. Boston College, Boston, MA (2003).
Snijders, T.A.B.: Models for longitudinal network data. In: Carrington, P., Scott, J., Wasserman, S. (ed) Models and Methods in Social Network. Analysis. Cambridge University Press, New York (2005).
Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford (2003).
Palla, G., Barabási, A-L., Vicsek, T.: Quantifying social group evolution. Nature 446 (5), 664–667 (2007).
Eckmann, J.-P., Moses, E., Sergi, D.: Entropy of dialogues creates coherent structures in e-mail traffic. PNAS 101 (40), 14333–14337 (2004).
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L.: Structure and tie strengths in mobile communication networks. PNAS 104 (18), 7332–7336 (2007).
Braha, D., Bar-Yam Y.: From centrality to temporary fame: Dynamic centrality in complex networks. Complexity 12 (2), 59–63 (2006).
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999).
Jin, E.M., Girvan, M., Newman, M.E.J.: Structure of growing social networks. Phys. Rev. E 64, 046132 (2001).
Roth, C.: Generalized Preferential Attachment: Towards Realistic Social Network Models. ISWC 4th Intl Semantic Web Conference. (2005).
Grönlund, A., Holme, P.: Networking the seceder model: Group formation in social and economic systems. Phys. Rev. E 70, 036108 (2004).
Boguña, M., Pastor-Satorras, R., Díaz-Guilera A., Arenas A.: Models of social networks based on social distance attachment. Phys Rev E 70, 056122 (2004).
Lazer, D.: The co-evolution of individual and network. J. Math. Sociol. 25, 69108 (2001).
Gross T., Blassius, B.: Adaptive coevolutionary networks: A review. J. R. Soc. Interface 5 (20), 259–271 (2007).
Skyrms, B., Pemantle, R.: A dynamic model of social network formation. Proc. Nat. Acad. Sci. 97 (16), 9340–9346 (2000).
Eguiluz, V.M., Zimmermann, M.G., Cela-Conde, C.J., San Miguel, M.: Cooperation and the emergence of role differentiation in the dynamics of social networks. AJS 110 (4), 9771008 (2005).
Biely, C., Dragosits, K., Thurner, S.: The prisoners dilemma on co-evolving networks under perfect rationality. Physica D 228, 4048 (2007).
Zimmermann, M.G., Eguíluz, V.M.: Cooperation, social networks, and the emergence of leadership in a prisoners dilemma with adaptive local interactions. Phys. Rev. E 72, 056118 (2005).
Zimmermann, M.G., Eguíluz, V.M., San Miguel, M.: Coevolution of dynamical states and interactions in dynamic networks. Phys. Rev. E 69, 065102(R) (2004).
Ebel, H., Bornholdt, S.: Coevolutionary games on networks. Phys. Rev. E 66, 056118 (2002).
Pacheco, J.M., Traulsen, A., Nowak, M.A.: Coevolution of strategy and structure in complex networks with dynamical linking. Phys. Rev. Lett. 97, 258103 (2006).
Rosvall, M., Sneppen, K.: Dynamics of opinions and social structures. arXiv:0708.0368v2 [physics.soc-ph] (2007).
Marsili, M., Vega-Redondo, F., Slanina, F.: The rise and fall of a networked society: A formal model. Proc. Nat. Acad. Sci. 101, 1439–1442 (2004).
Ehrhardt, G.C.M.A, Marsili, M., Vega-Redondo, F.: Phenomenological models of socioeconomic network dynamics. Phys. Rev. E 74, 036106 (2006).
Holme, P., Ghoshal, G.: Dynamics of networking agents competing for high centrality and low degree. Phys. Rev. Lett. 96, 098701 (2006).
König, M.D, Battiston, S., Napoletano, M., Schweitzer, F.: On algebraic graph theory and the dynamics of innovation networks. Networks and Heterogeneous Media 3(2) 201–220 (2007).
Rosvall, M., Sneppen, K.: Modeling self-organization of communication and topology in social networks. Phys. Rev. E 74, 016108 (2006).
Centola, D., González-Avella, J.C., Eguiíluz, V.M., San Miguel, M.: Homophily, cultural drift, and the co-evolution of cultural groups. J. of Conflict Resolution 51 (6), 905–929 (2007).
Axelrod, R.: The dissemination of culture: A model with local convergence and global polarization. The Journal of Conflict Resolution 41 (2), 203–226 (1997).
Benczik, I.J., Benczik, S.Z., Schmittmann, B., Zia, V.: Lack of consensus in social systems. EPL 82, 48006 (2007).
Vázquez, F., Eguíluz, V.M., San Miguel, M.: Generic absorbing transition in co-evolution dynamics. Phys. Rev. Lett. 100, 108702 (2007).
Zanette, D.H., Gil, S.: Opinion spreading and agent segregation on evolving networks. Phys. D 224, 156–165 (2006).
Gil, S., Zanette, D.H.: Coevolution of agents and networks: Opinion spreading and community disconnection. Phys. Lett. A 356, 89–95 (2006).
Liggett, T.M.: Interacting Particle Systems. Springer, New York (1985).
Holme, P., Newman, M.E.J.: Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74, 056108 (2006).
Gross, T., D'Lima, C.J.D., Blasius, B.: Epidemic dynamics on an adaptive network. Phys. Rev. Lett. 96, 208701 (2006).
Gross, T., Kevrekidis, I.G.: Coarse-graining adaptive coevolutionary network dynamics via automated moment closure. arXiv:nlin/0702047v1 [nlin.AO] (2007).
Zanette, D.: Coevolution of agents and networks in an epidemiological model. arXiv:0707.1249v2 [physics.soc-ph] (2007).
Sloot, P.M.A., Ivanov, S.V., Boukhanovsky, A.V., Vijver, D., Boucher, C.A.: Stochastic simulation of HIV population dynamics through complex network modeling, Int. J. of Computer Mathematics 85 (8), 1175–1187 (2008).
Borgatti, S.P., Molina, J.L.: Toward ethical guidelines for network research in organizations. Social Networks. 27(2), 107–117 (2005).
Birnbaum, M.H.: Methodological and ethical issues in conducting social psychology research via the Internet. In: Sansone, C., Morf, C.C., Panter, A.T. (ed) Handbook of Methods in Social Psychology. Sage, Thousand Oaks, CA (2004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Birkhäuser Boston, a part of Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Lozano, S. (2009). Dynamics of Social Complex Networks: Some Insights into Recent Research. In: Ganguly, N., Deutsch, A., Mukherjee, A. (eds) Dynamics On and Of Complex Networks. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4751-3_8
Download citation
DOI: https://doi.org/10.1007/978-0-8176-4751-3_8
Published:
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-4750-6
Online ISBN: 978-0-8176-4751-3
eBook Packages: Computer ScienceComputer Science (R0)