We build a search-and-matching algorithm of network dynamics with decision-making under incomplete information, seeking to understand the determinants of the observed gradual downgrading of expert opinion on complicated issues and the decreasing trust in science. Even without fake news,...

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model,...

Knowledge networks are important to understand learning in industry clusters but surprisingly little is known about what drives the formation, persistence and dissolution of ties. Applying stochastic actor-oriented models on longitudinal relational data from a mature cluster in a medium-tech...

The notion of "relativistic finance" became ingrained in the public imagination and has been asserted in many mass-media reports. However, despite an observed drive of the most reputable Wall Street firms to establish their servers ever closer to the trading hubs, there is surprisingly little...

Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze international research networks on the country level in different disease groups. Our empirical analysis is based on a unique dataset of...