Many network analysis and graph learning techniques are based on discrete-or continuous-
time models of random walks. To apply these methods, it is necessary to infer transition …

This unique textbook provides an introduction to statistical inference with network data. The
authors present a self-contained derivation and mathematical formulation of methods …

Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG),
from observational data is a statistically and computationally hard problem with essential …

• Want to request new functionality? We welcome suggestions–you can make a request by
filing an issue on the appropriate package GitHub repository. The chances that this …

Exponential random graph models are a class of widely used exponential family models for
social networks. The topological structure of an observed network is modelled by the relative …

Random graph models play a central role in network analysis. The Chung-Lu model, which
connects nodes based on their expected degrees, is of particular interest. It is widely used to …

With the growth of interest in network data across fields, the Exponential Random Graph
Model (ERGM) has emerged as the leading approach to the statistical analysis of network …

Doubly intractable distributions arise in many settings, for example, in Markov models for
point processes and exponential random graph models for networks. Bayesian inference for …

Social networks and other sparse data sets pose significant challenges for statistical
inference, since many standard statistical methods for testing model/data fit are not …