In my research I investigate dynamics in large social networks, networks of >1000 nodes. The most commonly used models have a number of limitations for studying the combined effects of both network and social parameters on network evolution; thus a year ago I started working with Rena Bakhshi, a very talented researcher within the Network Institute. Rena had a model- the mean field model, which had used to investigate dynamics of large communication networks and wanted to experiment with social network data. This resulted in our first application of the mean-field model for large social networks, entitled – Scalable Analysis for Large Social Networks: The Data-Aware Mean-Field Approach, recently published in Social Informatics. See the abstract here.

## Abstract

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a *mean-field model* which allows for the construction of a population-specific model informed from empirical research for predicting links from both network and social properties in large social networks.. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.