Clusters or communities can provide a coarse-grained description of complex systems at
multiple scales, but their detection remains challenging in practice. Community detection …

Community detection is a classic network problem with extensive applications in various
fields. Its most common method is using modularity maximization heuristics which rarely …

Mutual information is commonly used as a measure of similarity between competing
labelings of a given set of objects, for example to quantify performance in classification and …

Community detection, which involves partitioning nodes within a network, has widespread
applications across computational sciences. Modularity-based algorithms identify …

With the rapid development of internet technologies and the increasing availability of large-
scale data, the detection of community structures within complex networks has become a …

Community detection, a fundamental problem in computational sciences, finds applications
in various domains. Heuristics are often employed to detect communities through …

Community detection is a widely studied topic in network structure analysis. We propose a
community detection method based on the search for the local maxima of an objective …

When we detect communities in temporal networks it is important to ask questions about
how they change in time. Normalised Mutual Information (NMI) has been used to measure …

Multiplex graphs, characterised by their layered structure, exhibit informative
interdependencies within layers that are crucial for understanding complex network …

A random walk is a process in which a random walker takes consecutive steps in space at
equal intervals of time, with the length and direction of each step determined independently …