CRAN/E | CASCORE

CASCORE

Covariate Assisted Spectral Clustering on Ratios of Eigenvectors

Installation

About

Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) . In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.

arxiv.org/abs/2306.15616

Key Metrics

Version 0.1.2
Published 2023-07-02 292 days ago
Needs compilation? no
License GPL-2
CRAN checks CASCORE results

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Maintainer

Maintainer

Yaofang Hu

yaofangh@smu.edu

Authors

Yaofang Hu

aut / cre

Wanjie Wang

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

CASCORE archive

Imports

stats
pracma

Suggests

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