If you are curious about new approaches to studying large social network dynamics, check out the confirmed upcoming talk at the International Network for Social Network Analysis Conference (Sunbelt) in Hamburg, Germany. I will be giving a talk, on behalf of my co-authors (see below), on the “Methodological Specifications for Application of the Mean-field Model for Large Scale Social Networks” in the section- Large Scale Networks Analysis on Thursday afternoon 23 May 2013. In this talk I will discuss a model we have developed for overcoming a number of limitations in presently used models to investigate large social network dynamics.
For further details, as they become available check out – the conference webpage: http://hamburg-sunbelt2013.org/
Title: Methodological specifications for application of the mean-field model for large scale social networks
Author(s): Birkholz, Julie M1, Lungeanu, Alina,2, Bakhshi, Rena3, Groenewegen, Peter1, van Steen, Maarten3, Contractor, Noshir2
Institute(s): 1Vrije Universiteit Amsterdam, Network Institute, Organization Sciences, Amsterdam, Netherlands, 2Northwestern University, Evanston, IL, United States, 3Vrije Universiteit Amsterdam, Network Institute, Computer Science, Amsterdam, Netherlands
Text: The statistical modeling of the emergence of social networks is most commonly undertaken using two models: Stochastic actor-orient models (using SIENA) and p*/ERGM. However, both models have scaling limitations due to computational challenges. We propose the use of a mean-field approach to study large scale social network dynamics (1000s nodes). A mean-field model, originating from physics, enables consideration of a large number of nodes through the aggregation of classes of nodes into “nodal buckets.” The analysis then computes the interactions/communication between buckets. The mean-field model has been successfully applied to estimate attribute and network parameters on large social networks (Birkholz et al 2012). Here the nodes were aggregated into buckets based on shared attributes.
However, the inferences from such models hinges crucially on the selection of the shared attribute used for classification of nodes into buckets. To overcome this limitation, we propose a methodological specification (based on equivalence classes) for the aggregation of nodes into buckets. We apply this technique to study the the co-authorship network of 1,354 researchers in the Oncofertility scientific field over a four year period. We estimate the extent to which collaboration networks are influenced by multiple factors such as cosmopolitanism, visibility, scientific age, and institutional affiliation.