Map** the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

This paper surveys recent theoretical advances in convex optimization approaches for
community detection. We introduce some important theoretical techniques and results for …

We analyze the performance of spectral clustering for community extraction in stochastic
block models. We show that, under mild conditions, spectral clustering applied to the …

We study the problem of large-scale network embedding, which aims to learn latent
representations for network mining applications. Previous research shows that 1) popular …

Consider the problem of estimating the entries of a large matrix, when the observed entries
are noisy versions of a small random fraction of the original entries. This problem has …

This paper surveys some recent developments in fundamental limits and optimal algorithms
for network analysis. We focus on minimax optimal rates in three fundamental problems of …

Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …

Pseudo-likelihood methods for community detection in large sparse networks Page 1 The
Annals of Statistics 2013, Vol. 41, No. 4, 2097–2122 DOI: 10.1214/13-AOS1138 © Institute of …