This article discusses the stochastic actor-oriented model for analyzing panel data of
networks. The model is defined as a continuous-time Markov chain, observed at two or more …

Statistical models for social networks as dependent variables must represent the typical
network dependencies between tie variables such as reciprocity, homophily, transitivity, etc …

Interpersonal phenomena such as attachment, conflict, person perception, learning, and
influence have traditionally been studied by examining individuals in isolation, which falls …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

We propose to solve the link prediction problem in graphs using a supervised matrix
factorization approach. The model learns latent features from the topological structure of a …

The most promising class of statistical models for expressing structural properties of social
networks observed at one moment in time is the class of exponential random graph models …

Network models are widely used to represent relations between interacting units or actors.
Network data often exhibit transitivity, meaning that two actors that have ties to a third actor …

Dynamic networks are used in a variety of fields to represent the structure and evolution of
the relationships between entities. We present a model which embeds longitudinal network …

This paper examines the role of a person's generalized positive or negative feelings toward
someone (interpersonal affect) in task-related networks in organizations. We theorize that …