Abstract
The concept of a network is foundational to social science in general and to CSS in particular. How does CSS investigate networks across domains of social systems and processes? What do computational approaches add to the development of theory and research in social networks science? How can CSS deepen our understanding of social networks? This chapter introduces the reader to elements of social network analysis and computational applications to analyzing social complexity. The sections of this chapter cover formal aspects that have universal application to many different kinds of social networks, as well as applications to significant areas such as human cognition and decision-making, organizational models, the structure of small worlds, and international relations. From a formal perspective, the chapter highlights both mathematical and computational aspects of social networks. From a CSS perspective, social networks can be constructed via automated information extraction algorithms, based on ideas from Chap. 3. Social networks can also be used as a basis for analyzing social complexity and simulation models, as in the next chapters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The field of social networks modeling and analysis is different from “the science of networks” developed by physicists. This chapters deals with social networks modeling and analysis as a field of CSS. This is because the subject matter of social networks always involves social entities, although, as in other areas of CSS, the origin of the methodologies may come from a variety of disciplines.
- 2.
Freeman (2004, 2011) provides an extensive and highly recommended history of social network analysis. In addition, most major works in SNA include historical essays or notes. However, other significant connections to applied mathematics or complexity science have often been missed.
- 3.
This is the gist of the Königsberg bridges problem: is it possible to follow a path that crosses each of the seven city bridges exactly once, returning to the same point of departure? The answer is no, due to the presence of odd-degree nodes (a term defined later in this chapter). Note that the referent system for the Königsberg bridge problem is an interesting example of a coupled socio-natural-technological system composed of denizens, land, river, and bridges, respectively.
- 4.
In 1960 mathematicians Paul Erdős and Alfréd Rényi published their own paper on random graphs, reinventing the wheel nine years after Rapoport's seminal publication, and proposing new results.
- 5.
In 1999 the same mechanism of preferential attachment was re-proposed for the emergence of scaling in random networks (Barabasi and Albert 1999), decades after Anatol Rapoport's work on biased networks.
- 6.
The Watts-Strogatz model is d-regular with Var(d)=0, a class of very rare social networks (Wasserman and Faust 1994: 100–101). Terminology and notation are confused by physicists using the symbol k to denote node degree δ. Other physics terms for node degree δ include number of neighbors, node connectivity, nearest neighbors, wired vertices, and so on, which is reminiscent of the Tower of Babel lamented by social scientists (Sartori 1970; Collier and Gerring 2009). Node degree δ is the standard terminology of SNA used here.
- 7.
Note the formal mathematical translation of social entities into graph-theoretic nodes and social relations into edges.
- 8.
A classic example of complementary models of the same phenomenon are the wave model and the particle model of light.
- 9.
The first four network structures represented in Fig. 4.4—known as the chain, the wheel, the Y, and the circle—can be called Bavelas networks, after the MIT social psychologist who first investigated their properties in the context of communication networks.
- 10.
See Amaral et al. (2000) for an excellent survey of the main classes of small-world networks.
- 11.
From a graph-theoretic perspective, see Busacker and Saaty (1965: Chap. 5), Wilson (1985).
- 12.
Etymologically speaking, the term “dynamic” should be reserved for analysis of change as a function of forces of some kind, as indicated by the Greek root dynamos—which means force. The term kinematic or kinetic also means change, but without attribution to or explicit treatment of causal forces. Loosely speaking, unfortunately, it has become common in social science to call dynamic anything that changes with time. The proper term in “kinetic” or “kinematic.”
- 13.
“Pajek” means spider in Slovenian, referring to the web-like metaphor of a social network.
Recommended Readings
R.P. Abelson, Modes of resolution of belief dilemmas. J. Confl. Resolut. 3(4), 343–352 (1959)
L.A.N. Amaral, A. Scala, M. Barthélémy, H.E. Stanley, Classes of small-world networks. Proc. Natl. Acad. Sci. USA 97(21), 11149–11152 (2000)
D.L. Banks, K.M. Carley, Models for network evolution. J. Math. Sociol. 21(1–2), 173–196 (1996)
P. Bearman, J. Moody, R. Faris, Networks and history. Complexity 8(1), 61–71 (2003)
S.P. Borgatti, M.G. Everett, J.C. Johnson, Analyzing social networks (Los Angeles, Sage, 2013)
S.P. Borgatti, A. Mehra, D.J. Brass, G. Labianca, Network analysis in the social sciences. Science 323, 892–895 (2009)
R. Breiger, K.M. Carley, P. Pattison (eds.), Dynamic Social Network Modeling and Simulation: Workshop Summary and Papers (The National Academies Press, Washington, 2003)
K.M. Carley, Smart agents and organizations of the future, in The Handbook of New Media, ed. by L. Lievrouw, S. Livingstone (Sage, Thousand Oaks, 2001)
K.M. Carley, D. Columbus, P. Landwehr, AutoMap User's Guide 2013. Technical Report, CMU-ISR-13-105, School of Computer Science, Institute for Software Research, Carnegie Mellon University (2013a)
K.M. Carley, J. Pfeffer, J. Reminga, J. Storrick, D. Columbus ORA User's Guide 2013. Technical Report, CMU-ISR-13-108, School of Computer Science, Institute for Software Research, Carnegie Mellon University (2013b)
M. Coscia, F. Giannotti, D. Pedreschi, Towards democratic group detection in complex networks, in SBP 2012, ed. by Yang et al. (Springer, Heidelberg, 2012), pp. 105–113
C. Freeman Linton, The Development of Social Network Analysis: A Study in the Sociology of Science (Empirical Press, Vancouver, 2004)
D.L. Hansen, B. Shneiderman, M.A. Smith, Analyzing Social Media Networks with NodeXL: Insights from a Connected World (Elsevier, Amsterdam, 2011)
Z. Maoz, Networks of Nations: The Evolution, Structure, and Impact of International Networks, 1816–2001 (Cambridge University Press, Cambridge, 2010)
J. Moody, D. McFarland, S. Bender-deMoll, Dynamic network visualization. Am. J. Sociol. 110(4), 1206–1241 (2005)
J. Scott, P.J. Carrington (eds.), The SAGE Handbook of Social Network Analysis (Los Angeles, Sage, 2013)
M.A. Serrano, M. Boguna, A. Vespignani, Patterns of dominant flows in the world trade web. J. Econ. Interact. Coord. 2(2), 111–124 (2007)
J. Skvoretz, Complexity theory and models for social networks. Complexity 8(1), 47–55 (2003)
S. Wasserman, K. Faust, Social Network Analysis (Cambridge University Press, Cambridge, 1994)
D.J. Watts, Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, Princeton, 1999)
D.J. Watts, The new science of networks. Annu. Rev. Sociol. 30, 243–270 (2004)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag London
About this chapter
Cite this chapter
Cioffi-Revilla, C. (2014). Social Networks. In: Introduction to Computational Social Science. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5661-1_4
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5661-1_4
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5660-4
Online ISBN: 978-1-4471-5661-1
eBook Packages: Computer ScienceComputer Science (R0)