Fundamental limits of symmetric low-rank matrix estimation
We consider the high-dimensional inference problem where the signal is a low-rank
symmetric matrix which is corrupted by an additive Gaussian noise. Given a probabilistic …
symmetric matrix which is corrupted by an additive Gaussian noise. Given a probabilistic …
Recovering asymmetric communities in the stochastic block model
F Caltagirone, M Lelarge… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
We consider the sparse stochastic block model in the case where the degrees are
uninformative. The case where the two communities have approximately the same size has …
uninformative. The case where the two communities have approximately the same size has …
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
L Dall'Amico, R Couillet… - Journal of Statistical …, 2021 - iopscience.iop.org
This article unveils a new relation between the Nishimori temperature parametrizing a
distribution P and the Bethe free energy on random Erdős–Rényi graphs with edge weights …
distribution P and the Bethe free energy on random Erdős–Rényi graphs with edge weights …
Robust spectral detection of global structures in the data by learning a regularization
P Zhang - Advances in Neural Information Processing …, 2016 - proceedings.neurips.cc
Spectral methods are popular in detecting global structures in the given data that can be
represented as a matrix. However when the data matrix is sparse or noisy, classic spectral …
represented as a matrix. However when the data matrix is sparse or noisy, classic spectral …
Mixed membership graph clustering via systematic edge query
This work considers clustering nodes of a largely incomplete graph. Under the problem
setting, only a small amount of queries about the edges can be made, but the entire graph is …
setting, only a small amount of queries about the edges can be made, but the entire graph is …
Fast randomized semi-supervised clustering
We consider the problem of clustering partially labeled data from a minimal number of
randomly chosen pairwise comparisons between the items. We introduce an efficient local …
randomly chosen pairwise comparisons between the items. We introduce an efficient local …
[PDF][PDF] 基于非负矩阵分解的稀疏网络社区发现算法
金红, 胡智群 - 电子学报, 2023 - ejournal.org.cn
社区结构是复杂网络的重要特征之一, 社区发现对研究网络结构有重要的应用价值.
基于非负矩阵分解(Non-negative Matrix Factorization, NMF) 的社区发现方法是解决社区发现 …
基于非负矩阵分解(Non-negative Matrix Factorization, NMF) 的社区发现方法是解决社区发现 …
Spectral bounds for the Ising ferromagnet on an arbitrary given graph
We revisit classical bounds of Fisher on the ferromagnetic Ising model (Fisher 1967 Phys.
Rev. 162 480), and show how to efficiently use them on an arbitrary given graph to …
Rev. 162 480), and show how to efficiently use them on an arbitrary given graph to …
Fundamental limits of inference: A statistical physics approach.
L Miolane - 2019 - hal.science
We study classical statistical problems such as as community detection on graphs, Principal
Component Analysis (PCA), sparse PCA, Gaussian mixture clustering, linear and …
Component Analysis (PCA), sparse PCA, Gaussian mixture clustering, linear and …
Spectral inference methods on sparse graphs: theory and applications
A Saade - arXiv preprint arXiv:1610.04337, 2016 - arxiv.org
In an era of unprecedented deluge of (mostly unstructured) data, graphs are proving more
and more useful, across the sciences, as a flexible abstraction to capture complex …
and more useful, across the sciences, as a flexible abstraction to capture complex …