Modelling the Evolution of Continuously-Observed Networks: Communication in a Facebook-Like Community

Develops a conditional logistic framework for modeling tie creation in continuously-observed networks, enabling simultaneous estimation of multiple growth mechanisms (homophily, reciprocity, triadic closure, popularity). Applied to a Facebook-like community, the method advances beyond panel-data approaches to capture the fine-grained temporal dynamics of network evolution.

Models how networks grow through multiple simultaneous mechanisms including homophily, reciprocity, triadic closure, and popularity, demonstrating that network evolution cannot be reduced to any single explanatory principle. The continuous-observation framework reveals temporal dynamics that panel-data snapshots miss, showing that when you look at a network determines what you see.