Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

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 …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

Statistical node clustering in discrete time dynamic networks is an emerging field that raises
many challenges. Here, we explore statistical properties and frequentist inference in a …

This paper presents a survey of previous studies done on the problem of tracking community
evolution over time in dynamic social networks. This problem is of crucial importance in the …

Significant efforts have gone into the development of statistical models for analyzing data in
the form of networks, such as social networks. Most existing work has focused on modeling …