We provide the first information theoretical tight analysis for inference of latent community
structure given a sparse graph along with high dimensional node covariates, correlated with …

Community detection in graphs is the problem of finding groups of vertices which are more
densely connected than they are to the rest of the graph. This problem has a long history, but …

We consider the task of learning latent community structure from multiple correlated
networks. First, we study the problem of learning the latent vertex correspondence between …

We give upper and lower bounds on the information-theoretic threshold for community
detection in the stochastic block model. Specifically, consider a symmetric stochastic block …

We consider the problem of learning latent community structure from multiple correlated
networks. We study edge-correlated stochastic block models with two balanced …

The community detection problem involves making inferences about node labels in a graph,
based on observing the graph edges. This paper studies the effect of additional …

We consider the problem of recovering the community structure in the stochastic block
model with two communities. We aim to describe the mutual information between the …

The goal of this book is to present new mathematical techniques for studying the behaviour
of mean-field systems with disordered interactions. We mostly focus on certain problems of …

There has been a recent interest in understanding the power of local algorithms for
optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) …

Label spreading is a general technique for semi-supervised learning with point cloud or
network data, which can be interpreted as a diffusion of labels on a graph. While there are …