Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

Many network systems are composed of interdependent but distinct types of interactions,
which cannot be fully understood in isolation. These different types of interactions are often …

Multilayer networks are a useful data structure for simultaneously capturing multiple types of
relationships between a set of nodes. In such networks, each relational definition gives rise …

Spectral and matrix factorization methods for consistent community detection in multi-layer
networks Page 1 The Annals of Statistics 2020, Vol. 48, No. 1, 230–250 https://doi.org/10.1214/18-AOS1800 …

This insightful book theorizes the contrast between two logics of organization: bureaucracy
and collegiality. Based on this theory and employing a new methodology to transform our …

Multilayer networks continue to gain significant attention in many areas of study, particularly
due to their high utility in modeling interdependent systems such as critical infrastructures …

Multilayer graphs are commonly used for representing different relations between entities
and handling heterogeneous data processing tasks. Nonstandard multilayer graph …

This article deals with nonobserved dyads during the sampling of a network and consecutive
issues in the inference of the stochastic block model (SBM). We review sampling designs …

Research on behavior regulation was carried out after several months of social isolation,
provoked by the pandemic, between the months of February and March 2020. In spring …

Modern multi-layer networks are commonly stored and analyzed in a local and distributed
fashion because of the privacy, ownership, and communication costs. The literature on the …