Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …

Attributed graph embedding, which learns vector representations from graph topology and
node features, is a challenging task for graph analysis. Recently, methods based on graph …

Graph clustering aims to discover community structures in networks, the task being
fundamentally challenging mainly because the topology structure and the content of the …

The development of new technologies for map** structural and functional brain
connectivity has led to the creation of comprehensive network maps of neuronal circuits and …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Stochastic blockmodels have been proposed as a tool for detecting community structure in
networks as well as for generating synthetic networks for use as benchmarks. Most …

The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Networks, also called graphs by mathematicians, provide a useful abstraction of the
structure of many complex systems, ranging from social systems and computer networks to …