Community detection and stochastic block models: recent developments
E Abbe - Journal of Machine Learning Research, 2018 - jmlr.org
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 …
employed as a canonical model to study clustering and community detection, and provides …
Quantized neural networks: Training neural networks with low precision weights and activations
The principal submatrix localization problem deals with recovering a K× K principal
submatrix of elevated mean µ in a large n× n symmetric matrix subject to additive standard …
submatrix of elevated mean µ in a large n× n symmetric matrix subject to additive standard …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
[HTML][HTML] Entrywise eigenvector analysis of random matrices with low expected rank
Recovering low-rank structures via eigenvector perturbation analysis is a common problem
in statistical machine learning, such as in factor analysis, community detection, ranking …
in statistical machine learning, such as in factor analysis, community detection, ranking …
Exact recovery in the stochastic block model
The stochastic block model with two communities, or equivalently the planted bisection
model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric …
model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric …
Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery
E Abbe, C Sandon - 2015 IEEE 56th Annual Symposium on …, 2015 - ieeexplore.ieee.org
New phase transition phenomena have recently been discovered for the stochastic block
model, for the special case of two non-overlapping symmetric communities. This gives raise …
model, for the special case of two non-overlapping symmetric communities. This gives raise …
A neural collapse perspective on feature evolution in graph neural networks
Graph neural networks (GNNs) have become increasingly popular for classification tasks on
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
Achieving optimal misclassification proportion in stochastic block models
Community detection is a fundamental statistical problem in network data analysis. In this
paper, we present a polynomial time two-stage method that provably achieves optimal …
paper, we present a polynomial time two-stage method that provably achieves optimal …