The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

20 years of network community detection | Nature Physics Skip to main content Thank you for
visiting nature.com. You are using a browser version with limited support for CSS. To obtain the …

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Traditionally, community detection in graphs can be solved using spectral methods or
posterior inference under probabilistic graphical models. Focusing on random graph …

Two architectures that generalize convolutional neural networks (CNNs) for the processing
of signals supported on graphs are introduced. We start with the selection graph neural …

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 …

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 …

Recovering low-rank structures via eigenvector perturbation analysis is a common problem
in statistical machine learning, such as in factor analysis, community detection, ranking …