doc_centrality: Find a specified document centrality metric

In text2map: R Tools for Text Matrices, Embeddings, and Networks

Find a specified document centrality metric

Description

Given a document-term matrix or a document-similarity matrix, this function returns specified text network-based centrality measures. Currently, this includes weighted degree, eigenvector, betweenness, and spanning.

Usage

doc_centrality(mat, method, alpha = 1L, scale = FALSE, two_mode = TRUE)

Arguments

mat	Document-term matrix with terms as columns or a document-similarity matrix with documents as rows and columns.
method	Character vector indicating centrality method, including weighted degree, eigenvector, spanning, and betweenness.
alpha	Number (default = 1) indicating the tuning parameter for weighted metrics.
scale	Logical (default = FALSE), indicating whether to scale output.
two_mode	Logical (default = TRUE), indicating whether the input matrix is two mode (i.e. a document-term matrix) or one-mode (i.e. document-similarity matrix)

Details

If a document-term matrix is provided, the function obtains the one-mode document-level projection to get the document-similarity matrix using tcrossprod(). If a one-mode document-similarity matrix is provided, then this step is skipped. This way document similiarities may be obtained using other methods, such as Word-Mover's Distance. The diagonal is ignored in all calculations.

Value

A dataframe with two columns

Author(s)

Dustin Stoltz

text2map documentation built on July 9, 2023, 6:35 p.m.