There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

Modeling heterogeneity in random graphs through latent space models: a selective review\*
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …

Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be
characterized by local dependence in the sense that units which are close in a well-defined …

We review the broad range of recent statistical work in social network models, with emphasis
on computational aspects of these methods. Particular focus is applied to exponential-family …

We consider a statistical model for directed network formation that features both node-
specific parameters that capture degree heterogeneity and common parameters that reflect …

Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

This snapshot of the current frontier of statistics and network analysis focuses on the
foundational topics of modeling, sampling, and design. Primarily for graduate students and …

Recently, there has been much interest in the community structure or mesoscale
organization of complex networks. This structure is characterised either as a set of sparsely …

Mixture models are probabilistic models aimed at uncovering and representing latent
subgroups within a population. In the realm of network data analysis, the latent subgroups of …

In many applications of network analysis, it is important to distinguish between observed and
unobserved factors affecting network structure. To this end, we develop spectral estimators …