… analysis rather than on the comparatively new and quickly evolving world of inferential analysis
with networks. … In this book, we aim to introduce the reader to inferential network models …

… goal, statistically valid inference in the presence of … for inferential network analysis—the
quadratic assignment procedure, exponential random graph models, and latent space network …

Methods for descriptive network analysis have … inference with network data remain fledgling
by comparison. We introduce and evaluate a general model for inference with network data, …

… We analyzed the data using inferential network analysis (temporal exponential random
graph modeling, TERGM) accounting for self-organizing network forces. People high in …

… We introduce a Bayesian approach to conduct inferential analyses on dyadic data while
accounting … , principled inferential network analysis for a wide range of social science questions. …

… We then apply techniques of Social Network Analysis (SNA) and exponential random graph
… Drawing on inferential network analyses, we were particularly interested in testing the …

… The approach employs social network concepts and exponential random graph model (ERGM)
techniques. The analysis is focused on tie-formation processes in sponsor networks …

… of colleagues (ie, the network that shows how people know one … network to enhance the
internal validity of the study. Using exponential random graph modeling, all seven networks …

… -scales, resolves common inferential problems and enables many new types of analyses.
We provide a guide to time-ordered network analysis that includes computational resources, …

… threats to making inferences about networks as well as identify techniques that can be used
to mitigate potential bias. The core of our enterprise is that causal inference in any science is …