Abstract not found

a b s t r a c t This paper examined the relative influence of selection and socialization on alcohol and tobacco use in adolescent peer networks and peer groups. The sample included 1419 Finnish secondary education students (690 males and 729 females, mean age 16 years at the outset) from nine schools. Participants identified three school friends and described their alcohol and tobacco use on two occasions one year apart. Actor-based models simultaneously examined changes in peer network ties and changes in individual behaviors for all participants within each school. Multi-level analyses examined changes in individual behaviors for adolescents entering new peer groups and adolescents in stable peer groups, both of which were embedded within the school-based peer networks. Similar results emerged from both analytic methods: Selection and socialization contributed to similarity of alcohol use, but only selection was a factor in tobacco use. Ó

We propose the first network-based theory of alliance formation. Our theory suggests that, in addition to key state and dyad attributes already established by the literature, the evolution of the alliance network from any given point in time is largely determined by its structure. Specifically, we argue that closed triangles in the alliance network – where i is allied with j is allied with k is allied with i – produce synergy effects in which node-level utility is greater than the sum of its dyadic parts. This idea can be generalized to n-node closure, and, when considered along with factors which make dyadic alliance formation more attractive such as military prowess and political compatibility, suggests that the network will evolve towards a state of several densely connected clusters of states with star-like groupings of states as an intermediary stage. To evaluate our theory, we use the SIENA model for longitudinal network analysis and find that the roles of our network effects are robustly supported by the data whereas the effects of non-network parameters vary substantially between periods of recent history. Our results indicate that network structure plays a more central role in the formation

simulation analysis, the financial and intellectual support from the Collaboratory for Research on Global Projects at

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. 1

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

In this paper, we study the formation of network ties between Örms along the life cycle of a creative industry. We focus on three drivers of network formation: i) network endogeneity which stresses a path-dependent change originating from previous network structures, ii) Öve forms of proximity (e.g. geographical proximity) which ascribe tie formation to the similarity of actorsí attributes; and (iii) individual characteristics which refer to the heterogeneity in actors capabilities to exploit external knowledge. The paper employs a stochastic actor-oriented model to estimate the ñ changing ñ e§ects of these drivers on inter-Örm network formation in the global video game industry from 1987 to 2007. Our Öndings indicate that the e§ects of the drivers of network formation change with the degree of maturity of the industry. To an increasing extent, video game Örms tend to partner over shorter distances and with more cognitively similar Örms as the industry evolves.

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

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