Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

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 …

This chapter describes the basic variants of the stochastic blockmodel (SBM), and a
consistent Bayesian formulation that allows readers to infer them from data. The focus is on …

The empirical validation of community detection methods is often based on available
annotations on the nodes that serve as putative indicators of the large-scale network …

Complex systems are extremely hard to predict due to its highly nonlinear interactions and
rich emergent properties. Thanks to the rapid development of network science, our …

We address the problem of semi-supervised learning in relational networks, networks in
which nodes are entities and links are the relationships or interactions between them …

Despite the popularity of graph clustering, existing methods are haunted by two problems.
One is the implicit assumption that all attributes are treated equally with the same weights …

Real-world networks are dynamic in that both the state of the network components and the
structure of the network (topology) change over time. Most studies regarding network …

Many real-world systems are characterized by stochastic dynamical rules where a complex
network of interactions among individual elements probabilistically determines their state …

Networks are real systems modelled through mathematical objects made up of nodes and
links arranged into peculiar and deliberate (or partially deliberate) topologies. Studying …