The development of stochastic models for the analysis of social networks is an important
growth area in contemporary statistics. The last few decades have witnessed the rapid …

The uncertainty underlying real-world phenomena has attracted attention toward statistical
analysis approaches. In this regard, many problems can be modeled as networks. Thus, the …

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

We examine the conditions under which knowledge embedded in advice relations is likely to
reach across intraorganizational boundaries and be shared between distant organizational …

This paper compares several imputation methods for missing data in network analysis on a
diverse set of simulated networks under several missing data mechanisms. Previous work …

This paper compares several missing data treatment methods for missing network data on a
diverse set of simulated networks under several missing data mechanisms. We focus the …

Gossip is universal, and multiple studies have demonstrated that it can have beneficial
group-level outcomes when negative reports help identify defectors or norm-violators …

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 …

Descriptive neural network analyses have provided important insights into the organization
of structural and functional networks in the human brain. However, these analyses have …

We extend the well-known and widely used exponential random graph model (ERGM) by
including nodal random effects to compensate for heterogeneity in the nodes of a network …