Community detection in node-attributed social networks: a survey
P Chunaev - Computer Science Review, 2020 - Elsevier
Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
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] Structure and inference in annotated networks
MEJ Newman, A Clauset - Nature communications, 2016 - nature.com
For many networks of scientific interest we know both the connections of the network and
information about the network nodes, such as the age or gender of individuals in a social …
information about the network nodes, such as the age or gender of individuals in a social …
Contextual stochastic block models
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 …
structure given a sparse graph along with high dimensional node covariates, correlated with …
Understanding non-linearity in graph neural networks from the bayesian-inference perspective
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs
due to their impressive capability of capturing nonlinear relations in graph-structured data …
due to their impressive capability of capturing nonlinear relations in graph-structured data …
Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
Recently there has been increased interest in semi-supervised classification in the presence
of graphical information. A new class of learning models has emerged that relies, at its most …
of graphical information. A new class of learning models has emerged that relies, at its most …
A survey on theoretical advances of community detection in networks
Y Zhao - Wiley Interdisciplinary Reviews: Computational …, 2017 - Wiley Online Library
Real‐world networks usually have community structure, that is, nodes are grouped into
densely connected communities. Community detection is one of the most popular and best …
densely connected communities. Community detection is one of the most popular and best …
Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: combining two-layer LMDI method with spectral clustering
L Wen, Z Li - Science of the Total Environment, 2020 - Elsevier
Understanding the CO 2 (carbon dioxide) emissions mechanisms in each province is
important to reduce China's CO 2 emissions and achieve carbon reduction targets. This …
important to reduce China's CO 2 emissions and achieve carbon reduction targets. This …
An theory of PCA and spectral clustering
An lp theory of PCA and spectral clustering Page 1 The Annals of Statistics 2022, Vol. 50, No.
4, 2359–2385 https://doi.org/10.1214/22-AOS2196 © Institute of Mathematical Statistics, 2022 …
4, 2359–2385 https://doi.org/10.1214/22-AOS2196 © Institute of Mathematical Statistics, 2022 …
Understanding regularized spectral clustering via graph conductance
This paper uses the relationship between graph conductance and spectral clustering to
study (i) the failures of spectral clustering and (ii) the benefits of regularization. The …
study (i) the failures of spectral clustering and (ii) the benefits of regularization. The …