  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 extensions proposed for different aspects in this area, such as
the type of the graph, the clustering approach, the inference approach, and
whether the number of groups is selected or estimated. We also review models
that combine block modelling with topic modelling and/or longitudinal
modelling, regarding how these models deal with multiple types of data. How
different approaches cope with various issues will be summarised and compared,
to facilitate the demand of practitioners for a concise overview of the current
status of these areas of literature.
