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 degree, eigenvector, betweenness, and spanning.

Usage

doc_centrality(mat, method, alpha = 1L, 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 "degree", "eigen", "span", and "between".
alpha	Number (default = 1) indicating the tuning parameter for weighted metrics.
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 similarities may be obtained using other methods, such as Word-Mover's Distance (see doc_similarity). The diagonal is ignored in all calculations.

Document centrality methods include:

• degree: Opsahl's weighted degree centrality with tuning parameter "alpha"

• between: vertex betweenness centrality using Brandes' method

• eigen: eigenvector centrality using Freeman's method. When the top two eigenvalues are tied or nearly tied (e.g. a corpus with duplicate or highly symmetric documents), the leading eigenvector is only defined up to an arbitrary rotation within that tied subspace, and results can be unstable under tiny perturbations to the similarity matrix; prefer "degree" or "between" for such corpora.

• span: Modified Burt's constraint following Stoltz and Taylor's method, uses a tuning parameter "alpha" and the output is scaled.

Value

A dataframe with two columns

Author(s)

Dustin Stoltz

References

Brandes, Ulrik (2000) 'A faster algorithm for betweenness centrality' Journal of Mathematical Sociology. 25(2):163-177 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/0022250X.2001.9990249")}.
Opsahl, Tore, et al. (2010) 'Node centrality in weighted networks: Generalizing degree and shortest paths.' Social Networks. 32(3)245:251 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2010.03.006")}
Stoltz, Dustin; Taylor, Marshall (2019) 'Textual Spanning: Finding Discursive Holes in Text Networks' Socius. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/2378023119827674")}

Examples

# load example text
data(jfk_speech)

# minimal preprocessing
jfk_speech$sentence <- tolower(jfk_speech$sentence)
jfk_speech$sentence <- gsub("[[:punct:]]+", " ", jfk_speech$sentence)

# create DTM
dtm <- dtm_builder(jfk_speech, sentence, sentence_id)

ddeg <- doc_centrality(dtm, method = "degree")
if (requireNamespace("RSpectra", quietly = TRUE)) {
  deig <- doc_centrality(dtm, method = "eigen")
}
dbet <- doc_centrality(dtm, method = "between")
dspa <- doc_centrality(dtm, method = "span")

# with a document-similarity matrix (dsm)

dsm <- doc_similarity(dtm, method = "cosine")
ddeg <- doc_centrality(dsm, method = "degree", two_mode = FALSE)

text2map documentation built on Sept. 23, 2026, 5:07 p.m.