Abstract
This paper presents a model of social network evolution, to predict and simulate changes in social networks induced by lifecycle events. We argue that social networks change with lifecycle events, and we extend a model of friendship selection to incorporate these dynamics of personal social networks. The model uses theories of homophily and reciprocity and is formulated in a random utility maximization framework to predict the formation of social ties between individuals in the population. It is then extended to predict the evolution of social networks in response to life cycle events. The model is estimated using attribute data of a national sample and an event-based retrospective dataset collected in 2009 and 2011 respectively. Findings suggest that homophily has a strong effect on the formation of new ties. However, heterophily also plays a role in maintaining existing ties. Although the motivation of this research stems from incorporating social network dynamics in large-scale travel behaviour micro-simulation models, the research can be used in a variety of fields for similar purposes.
Highlights
Analysis of social travel and social interaction patterns has become an important part of transportation research in recent years
We argue that social networks change with lifecycle events, and we extend a model of friendship selection to incorporate these dynamics of personal social networks
The motivation of this research stems from incorporating social network dynamics in large-scale travel behaviour micro-simulation models, the research can be used in a variety of fields for similar purposes
Summary
Analysis of social travel and social interaction patterns has become an important part of transportation research in recent years. Berg et al (2009, 2010, 2013) ; Axhausen 2008) These studies addressed social networks and travel choices at a given time or age of an individual. Social networks evolve (Snijders et al 2010; Sharmeen et al 2010) over time and life stages. Social networks and activity-travel patterns co-evolve (Sharmeen et al 2014a). Ignoring such interdependency may lead to biases in prediction and erroneous design and output of models for policy evaluation. A challenge in travel behaviour research is to model the dynamics of social networks
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