Why social networks

by Professor Garry Robins

Although the claim that social processes are interactive, dynamic, and socially-situated is neither new nor controversial, commentaries across social science disciplines continue to emphasise the need for a new understanding of social phenomena that takes these features seriously.  
By dynamic and interactive, we mean that the actions of one individual may both depend on and, in turn, influence the actions of others.  By socially-situated, we mean that actions depend on a multi-layered complex of social entities that includes social relations, group affiliations, social settings and spatial neighbourhoods. 
These aspects of social “location” both constrain and provide opportunities for possible future actions.  
Further, an action by one individual may change the context for other individuals in neighbouring locations, so that a dynamic, interactive characterisation of social processes necessarily implicates an understanding of social location.
The task of building models for such complex processes is difficult, both theoretically and technically, and few approaches have captured simultaneously their dynamic, interactive, and location-dependent qualities. 

 
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Why model Social networks

A fuller recognition of the interdependent nature of social processes brings with it a quantitative imperative: relatively precise characterisations of local interactive processes are required in order to understand their implications at an aggregate or global system level (eg group, community).   Small local changes can have dramatic global effects.

The guiding principle of our approach:

Network ties/social action are the outcome of unobserved local interactive processes. There are both regularities and irregularities in these local interactive processes.
With exponential random graph models, we can construct a stochastic model formulation in which:

  • assumptions about “local interactions” are explicit
  • regularities can be represented quantitatively and estimated from data
  • hypotheses about regularities can be tested
  • global consequences of local regularities can be understood (and provide an exacting approach to model evaluation)

Interest in social network applications is growing fast, bringing a demand for new network modelling techniques.Our commitment is to methodological development but with a clear linkage to empirical data and to meaningful applications.  Methodological advances also entail theoretical development. We are engaged in the linkages between local social processes and system-wide outcomes. A greater theoretical understanding of these linkages is an important step.

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EXPONENTIAL Random graph models

In recent years, there has been growing interest in exponential random graph models for social networks, commonly called the p* class of models.  These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of the earlier p1 model class (Holland & Leinhardt, 1981).  Accordingly, they permit models to be built from a more realistic construal of the structural foundations of social behavior. 

Markov random graphs (Frank & Strauss, 1986) have been the most popular form of these models to date, but they have certain deficiencies.  We now have new specifications for exponential random graph models that go beyond Markov random graphs, and considerably open up the possibilities for the statistical examination of social networks (Snijders, Pattison, Robins & Handcock, 2005). 

Click here to obtain a bibliography and glossary on exponential random graph models.

For other resources on exponential random graph models, see the statnet webpage at the University of Washington and Tom Snijders' social networks page.