  The study of network formation is pervasive in economics, sociology, and many
other fields. In this paper, we model network formation as a `choice' that is
made by nodes in a network to connect to other nodes. We study these `choices'
using discrete-choice models, in which an agent chooses between two or more
discrete alternatives. We employ the `repeated-choice' (RC) model to study
network formation. We argue that the RC model overcomes important limitations
of the multinomial logit (MNL) model, which gives one framework for studying
network formation, and that it is well-suited to study network formation. We
also illustrate how to use the RC model to accurately study network formation
using both synthetic and real-world networks. Using edge-independent synthetic
networks, we also compare the performance of the MNL model and the RC model. We
find that the RC model estimates the data-generation process of our synthetic
networks more accurately than the MNL model. In a patent citation network,
which forms sequentially, we present a case study of a qualitatively
interesting scenario -- the fact that new patents are more likely to cite
older, more cited, and similar patents -- for which employing the RC model
yields interesting insights.
