Package {bibnets}

Title:	Importing, Constructing, and Exporting Bibliometric Networks
Version:	0.4.4
Description:	Imports, constructs, and exports bibliometric networks from scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref' exports. Goes beyond standard co-networks with attention-weighted networks (lead, last, proximity, circular position weights), position-aware counting (harmonic, arithmetic, geometric, golden-ratio), similarity and dissimilarity normalisations, temporal networks with fixed, sliding, and cumulative windows, disparity-filter backbone extraction, historiograph construction, and local citation scoring. Methods described in López-Pernas, Saqr & Apiola (2023) <doi:10.1007/978-3-031-25336-2_5>.
License:	MIT + file LICENSE
URL:	https://github.com/mohsaqr/bibnets
BugReports:	https://github.com/mohsaqr/bibnets/issues
Depends:	R (≥ 4.1.0)
Imports:	Matrix, stats, utils
Suggests:	cograph, igraph, knitr, openalexR, rcrossref, rmarkdown, testthat (≥ 3.0.0), tidygraph
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-05-16 07:39:30 UTC; mohammedsaqr
Author:	Mohammed Saqr [aut, cre, cph], Sonsoles López-Pernas [aut]
Maintainer:	Mohammed Saqr <saqr@saqr.me>
Repository:	CRAN
Date/Publication:	2026-05-20 09:10:14 UTC

Aggregate multi-valued fields by an entity

Description

Groups documents by a single-valued or list-column entity (e.g., author, journal) and pools all values from another list-column (e.g., references, keywords) across documents belonging to that entity.

Usage

aggregate_by_entity(data, entity_field, value_field, min_freq = 1L)

Arguments

Value

A data frame with columns id (entity name) and value_field (list-column of pooled values, with duplicates preserved).

Align columns before row-binding bibliographic files

Description

Align columns before row-binding bibliographic files

Usage

align_biblio_columns(dfs)

Apply counting weights to a generic bipartite matrix

Description

For position-independent counting of non-author fields (references, keywords, etc.). Modifies the bipartite matrix row weights.

Usage

apply_counting(B, counting = "full", network_type = "symmetric")

Arguments

Value

A weighted sparse matrix.

Build an author network

Description

Constructs a network between authors using one of four relationship types and any of 13 counting methods, including 9 position-dependent methods that respect author byline order.

Usage

author_network(
  data,
  type = "collaboration",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  position_weights = c(1, 0.8, 0.6, 0.4),
  first_last_weight = 2,
  attention = NULL,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(biblio_data)
author_network(biblio_data, "collaboration")
author_network(biblio_data, "collaboration", counting = "harmonic")
author_network(biblio_data, "collaboration", counting = "geometric",
               similarity = "association")

Compute positional author weights for a single paper

Description

Given the number of authors and their positions, returns a weight vector for positional counting methods. All methods normalize weights to sum to 1 per paper. Position-independent methods (fractional, paper, strength) are handled by apply_counting() instead.

Usage

author_weights(
  n,
  counting = "fractional",
  position_weights = c(1, 0.8, 0.6, 0.4),
  first_last_weight = 2
)

Arguments

Value

Numeric vector of length n, summing to 1.

Extract network backbone using the disparity filter

Description

Applies the disparity filter to a weighted edge list. For each edge, it computes an alpha (p-value) from both endpoints and keeps the edge if it is statistically significant from at least one endpoint.

Usage

backbone(edges, alpha = 0.05)

Arguments

Details

The null model asks: given that node i has total strength s_i distributed uniformly across k_i edges, what is the probability that a single edge weight is as large as w_{ij}? The answer is

\alpha_{ij} = \left(1 - \frac{w_{ij}}{s_i}\right)^{k_i - 1}

An edge is retained if \min(\alpha_{ij}, \alpha_{ji}) < \alpha. Nodes with only one edge always have \alpha = 0 and are always kept.

Value

The filtered edge data frame with an added alpha column (the minimum alpha from the two endpoints).

Examples

edges <- data.frame(
  from   = c("A", "A", "A", "B", "C"),
  to     = c("B", "C", "D", "C", "D"),
  weight = c(10,   1,   1,   8,   1)
)
backbone(edges, alpha = 0.05)

Example bibliometric dataset

Description

A small synthetic dataset of 10 scholarly papers with overlapping authors, references, and keywords. Designed for testing and demonstrating all network construction functions in bibnets.

Usage

biblio_data

Format

A data frame with 10 rows and 9 columns:

id

Unique document identifier (W1–W10).

title

Document title.

year

Publication year (2018–2022).

journal

Source journal (Scientometrics, Journal of Informetrics, JASIST, Quantitative Science Studies).

doi

DOI string.

cited_by_count

Times cited.

authors

List-column of author name strings (6 unique authors).

references

List-column of cited reference IDs (10 unique refs, R1–R10). Each paper cites exactly 4 references.

keywords

List-column of keyword strings (24 unique keywords). Each paper has 3 keywords.

Examples

data(biblio_data)
reference_network(biblio_data)
document_network(biblio_data, "coupling")
author_network(biblio_data, "collaboration")

Build a weighted bipartite matrix using positional counting

Description

For position-dependent counting methods, this replaces the binary bipartite matrix entries with positional weights derived from author order.

Usage

build_author_bipartite(
  data,
  field = "authors",
  counting = "full",
  position_weights = c(1, 0.8, 0.6, 0.4),
  first_last_weight = 2,
  deduplicate = TRUE
)

Arguments

Value

A sparse weighted bipartite matrix (works x authors).

Build a bipartite incidence matrix from bibliometric data

Description

Constructs a sparse works x entities two-mode matrix from a data frame with a list-column. This is the core engine behind all network construction functions.

Usage

build_bipartite(data, field, min_freq = 1L, deduplicate = TRUE)

Arguments

Value

A sparse dgCMatrix with rows = works (named by id) and columns = unique entities.

Build bipartite matrix from a long-format edge table

Description

Alternative constructor when data is already in long form (e.g., a two-column data frame of document-reference pairs).

Usage

build_bipartite_long(edges, min_freq = 1L)

Arguments

Value

A sparse dgCMatrix.

Build a network where entities share values from another field

Description

Build a network where entities share values from another field

Usage

build_by_network(
  data,
  field,
  by,
  counting,
  similarity,
  threshold,
  min_occur,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE
)

Build time window definitions

Description

Build time window definitions

Usage

build_windows(min_time, max_time, window, step, strategy)

Build a co-occurrence network from any field

Description

With one field, entities are linked when they co-occur in the same document. With by, entities are linked when they share values of the by field across documents.

Usage

conetwork(
  data,
  field,
  by = NULL,
  sep = ";",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Details

Fields can be list-columns (already split) or character columns with delimiters (auto-split via sep).

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(biblio_data)

# Co-occurrence: keywords appearing in the same document
conetwork(biblio_data, "keywords")

# Authors linked by shared keywords
conetwork(biblio_data, "authors", by = "keywords")

# Keywords linked by shared authors
conetwork(biblio_data, "keywords", by = "authors")

# Journals linked by shared references (= journal coupling)
conetwork(biblio_data, "journal", by = "references", similarity = "cosine")

# Auto-splits semicolon-delimited string columns
d <- data.frame(id = 1:3, tags = c("ml; dl; nlp", "ml; cv", "dl; cv"))
conetwork(d, "tags")

Build a country network

Description

Constructs a network between countries based on collaboration or coupling.

Usage

country_network(
  data,
  type = "collaboration",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  attention = NULL,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(open_alex_gold_open_access_learning_analytics)
country_network(open_alex_gold_open_access_learning_analytics, "collaboration")

Detect bibliometric file format

Description

Detect bibliometric file format

Usage

detect_format(file)

Arguments

Value

Character: format name or "unknown".

Build a document network

Description

Constructs a network between documents (papers) in the dataset.

Usage

document_network(
  data,
  type = "coupling",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format. For type = "citation", edges are directed (from = citing, to = cited) with weight and count both 1.

Examples

data(biblio_data)
document_network(biblio_data, "coupling")
document_network(biblio_data, "coupling", counting = "strength")

Convert edge list to a square sparse matrix

Description

Convert edge list to a square sparse matrix

Usage

edgelist_to_mat(edges, nodes = NULL, symmetric = TRUE)

Arguments

Value

A sparse dgCMatrix.

Ensure a column is a list-column, splitting if needed

Description

Ensure a column is a list-column, splitting if needed

Usage

ensure_list_column(data, field, sep = ";")

Filter edges to top-n nodes

Description

Keeps only edges between the most frequent nodes. Node frequency is determined by how many edges each node participates in.

Usage

filter_top(edges, n)

Arguments

Value

A filtered data frame with edges among the top n nodes.

Examples

data(biblio_data)
edges <- author_network(biblio_data, "collaboration")

# Keep only edges among the top 3 most connected authors
filter_top(edges, 3)

Build a historiograph (chronological citation network)

Description

Constructs a Garfield-style historiograph: a directed citation network among the most locally cited documents, laid out chronologically.

Usage

historiograph(data, n = 30, min_lcs = 1)

Arguments

Value

A list with:

⁠$nodes⁠

Data frame of included documents with id, lcs, gcs, year, title, journal, doi.

⁠$edges⁠

Data frame of directed citation edges with from (citing), to (cited), year_from, year_to.

Examples

data(biblio_data)
h <- historiograph(biblio_data, n = 5)
h$nodes
h$edges

Build an institution network

Description

Constructs a network between institutions (affiliations).

Usage

institution_network(
  data,
  type = "collaboration",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  attention = NULL,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(open_alex_gold_open_access_learning_analytics)
institution_network(open_alex_gold_open_access_learning_analytics, "collaboration")

Build a keyword co-occurrence network

Description

Constructs a network where two keywords are linked when they appear together in the same document.

Usage

keyword_network(
  data,
  field = "keywords",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  attention = NULL,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(biblio_data)
keyword_network(biblio_data)
keyword_network(biblio_data, similarity = "association")

Compute local citation scores

Description

Counts how many times each document is cited by other documents within the dataset.

Usage

local_citations(data)

Arguments

Value

A data frame with columns:

id

Document identifier.

lcs

Local Citation Score: times cited within the dataset.

gcs

Global Citation Score: cited_by_count if available.

Plus any metadata columns present in the input (title, year, journal, doi).

Examples

data(biblio_data)
local_citations(biblio_data)

Convert a sparse square matrix to a tidy edge list

Description

Extracts non-zero entries directly from the sparse representation — no dense matrix allocation. For undirected networks, only the upper triangle is returned.

Usage

mat_to_edgelist(A, directed = FALSE)

Arguments

Value

A data frame with columns from, to, weight, sorted by descending weight.

Construct a co-occurrence network via two-mode multiplication

Description

The unified engine for all bibliometric networks. Operates entirely in sparse representation — never allocates a dense n x n matrix.

Usage

multiply_bipartite(
  B,
  mode = "columns",
  similarity = "none",
  threshold = 0,
  top_n = NULL,
  self_loops = FALSE
)

Arguments

Value

A data frame with columns from, to, weight, count.

Normalize a co-occurrence matrix

Description

Applies a similarity normalization to a square co-occurrence matrix. The diagonal of the input matrix is used as the total occurrence count for each item. Operates entirely in sparse representation.

Usage

normalize(A, method = "none")

Arguments

Value

A normalized sparse matrix of the same dimensions.

Examples

# Create a small co-occurrence matrix
A <- matrix(c(10, 3, 1, 3, 8, 2, 1, 2, 5), nrow = 3,
            dimnames = list(c("a", "b", "c"), c("a", "b", "c")))
normalize(A, "association")
normalize(A, "cosine")
normalize(A, "jaccard")

OpenAlex Gold Open Access Learning Analytics dataset

Description

A corpus of 1,508 gold open-access scholarly works on learning analytics, retrieved from OpenAlex (CC0 licence). All records have a verified title, publication year, and at least one author. Journal names are present for works published in a named source; preprints and book chapters may have NA in journal.

Usage

open_alex_gold_open_access_learning_analytics

Format

A data frame with 1,508 rows and 11 columns:

id

OpenAlex work ID (e.g. "W2769342982").

title

Work title.

year

Publication year (integer).

journal

Source name, or NA if not available.

doi

DOI string without the https://doi.org/ prefix, or NA.

cited_by_count

Number of citing works as recorded in OpenAlex.

type

Work type ("article", "review", "preprint", "book-chapter", etc.).

authors

List-column of author display names (pipe-split from the OpenAlex flat export; one name per authorship slot).

keywords

List-column with one element: the primary OpenAlex topic for the work (e.g. "Online Learning and Analytics").

affiliations

List-column of institution display names (one entry per authorship–institution pair).

countries

List-column of two-letter ISO country codes (one entry per authorship–institution pair).

Source

OpenAlex https://openalex.org, CC0 licence.

Examples

data(open_alex_gold_open_access_learning_analytics)
d <- open_alex_gold_open_access_learning_analytics
author_network(d, "collaboration")
country_network(d, "collaboration")

List of all available counting methods

Description

List of all available counting methods

Usage

position_independent_counts()

Value

Character vector of method names.

Print a bibnets network edge list

Description

Print a bibnets network edge list

Usage

## S3 method for class 'bibnets_network'
print(x, n = 10L, ...)

Arguments

Value

Invisibly returns x.

Examples

data(biblio_data)
edges <- author_network(biblio_data, "collaboration")
print(edges)

Prune a weighted edge list

Description

Reduces a weighted edge list by removing weak or excess edges.

Usage

prune(edges, threshold = NULL, top_n = NULL)

Arguments

Value

The filtered edge data frame (same columns as input).

Examples

edges <- data.frame(
  from   = c("A","A","A","B","B","C"),
  to     = c("B","C","D","C","D","D"),
  weight = c(5,  1,  2,  4,  1,  3)
)

# Keep only edges with weight >= 3
prune(edges, threshold = 3)

# Keep the 2 strongest edges per node
prune(edges, top_n = 2)

Read bibliometric data

Description

Universal reader that handles files, folders, format detection, and generic CSV input. Accepts a single file, multiple files, or a directory.

Usage

read_biblio(path, format = "auto", id = NULL, actors = NULL, sep = ";", ...)

Arguments

Value

A data frame.

Examples

# Auto-detect format from file content (here: a bundled OpenAlex CSV)
f <- system.file("extdata", "openalex_works.csv", package = "bibnets")
data <- read_biblio(f)
head(data[, c("id", "title", "year", "journal")])

# Read multiple files at once; auto-detects each format
f_scopus <- system.file("extdata", "scopus_sample.csv", package = "bibnets")
f_wos    <- system.file("extdata", "wos_sample.txt",  package = "bibnets")
combined <- read_biblio(c(f_scopus, f_wos))
head(combined[, c("id", "title", "year", "journal")])

# Read every supported export in a directory (here: the bundled extdata)
folder <- system.file("extdata", package = "bibnets")
all_data <- read_biblio(folder)
nrow(all_data)

# Generic CSV: point read_biblio at any CSV and name the list-column fields
tmp <- tempfile(fileext = ".csv")
write.csv(data.frame(
  doc_id  = c("a", "b"),
  Authors = c("Smith J; Jones A", "Davis M"),
  Keywords = c("networks; bibliometrics", "analytics")
), tmp, row.names = FALSE)
generic <- read_biblio(tmp, format = "generic",
                       id = "doc_id",
                       actors = c("Authors", "Keywords"),
                       sep = ";")
head(generic)

Read a BibTeX file

Description

Parses a .bib file into a standardized bibliometric data frame. Note: standard BibTeX does not contain cited references, so the references column will be empty unless the file includes a non-standard cited-references or note field with reference data.

Usage

read_bibtex(file, encoding = "UTF-8")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references (typically empty for BibTeX), and keywords.

Examples

# Write a minimal BibTeX entry to a temp file, then read it
bib <- '@article{smith2020,
  title  = {Bibliometric networks},
  author = {Smith, J. and Jones, K.},
  journal = {Test Journal},
  year   = {2020},
  doi    = {10.1000/test}
}'
f <- tempfile(fileext = ".bib")
writeLines(bib, f)
data <- read_bibtex(f)
data[, c("id", "title", "year", "journal", "doi")]
unlink(f)

Convert Crossref API data to bibnets format

Description

Takes the output of rcrossref::cr_works() (the ⁠$data⁠ tibble/data frame) and converts it to the standardized bibnets format.

Usage

read_crossref(data)

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords.

Examples

# Construct a minimal data frame matching the structure of
# rcrossref::cr_works(...)$data. In practice, pass that data frame directly.
raw <- data.frame(
  doi = c("10.1/a", "10.2/b"),
  title = c("First paper", "Second paper"),
  issued = c("2022-01-01", "2021-06-15"),
  container.title = c("Journal A", "Journal B"),
  is.referenced.by.count = c("3", "9"),
  type = c("journal-article", "journal-article"),
  stringsAsFactors = FALSE
)
raw$author <- list(
  data.frame(given = c("Jane", "Anne"),
             family = c("Smith", "Jones"),
             stringsAsFactors = FALSE),
  data.frame(given = "Mark", family = "Davis", stringsAsFactors = FALSE)
)
data <- read_crossref(raw)
head(data[, c("id", "title", "year", "journal")])

Read Dimensions CSV export

Description

Parses a CSV file exported from Dimensions into a standardized bibliometric data frame.

Usage

read_dimensions(file, encoding = "UTF-8")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords. Dimensions-specific extras: affiliations (list-column), countries (list-column).

Examples

f <- system.file("extdata", "dimensions_sample.csv", package = "bibnets")
data <- read_dimensions(f)
head(data[, c("id", "title", "year", "journal")])

Read a generic CSV with user-specified columns

Description

Read a generic CSV with user-specified columns

Usage

read_generic(file, id = NULL, actors = NULL, sep = ";")

Read Lens.org CSV export

Description

Parses a CSV file exported from Lens.org into a standardized bibliometric data frame.

Usage

read_lens(file, encoding = "UTF-8")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords.

Examples

f <- system.file("extdata", "lens_sample.csv", package = "bibnets")
data <- read_lens(f)
head(data[, c("id", "title", "year", "journal")])

Convert OpenAlex data to bibnets format

Description

Takes the output of openalexR::oa_fetch() (a tibble/data frame of works) and converts it to the standardized bibnets format with list-columns.

Usage

read_openalex(data)

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords.

Examples

# Construct a minimal data frame matching the structure returned by
# openalexR::oa_fetch(entity = "works", ...). In practice, pass the
# result of oa_fetch() directly.
raw <- data.frame(
  id = c("W123", "W456"),
  display_name = c("First paper", "Second paper"),
  publication_year = c(2022L, 2021L),
  so = c("Journal A", "Journal B"),
  doi = c("https://doi.org/10.1/a", "https://doi.org/10.2/b"),
  cited_by_count = c(5L, 12L),
  stringsAsFactors = FALSE
)
raw$author <- list(
  data.frame(au_display_name = c("Smith J", "Jones A"),
             stringsAsFactors = FALSE),
  data.frame(au_display_name = "Davis M", stringsAsFactors = FALSE)
)
raw$referenced_works <- list(c("W100", "W200"), "W123")
data <- read_openalex(raw)
head(data[, c("id", "title", "year", "journal", "doi")])

Read a flat OpenAlex CSV export

Description

Reads the flat CSV format downloaded directly from the OpenAlex website (openalex.org/works exports). Multi-value fields are pipe-delimited (|). This is distinct from the nested tibble produced by openalexR::oa_fetch(), which is handled by read_openalex().

Usage

read_openalex_csv(file, sep = "|")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, keywords, affiliations, countries. abstract and references are always NA / empty (not available in the flat export).

Examples

f <- system.file("extdata", "openalex_works.csv", package = "bibnets")
data <- read_openalex_csv(f)

Read an RIS file

Description

Parses a .ris file into a standardized bibliometric data frame. Like BibTeX, standard RIS does not include cited references.

Usage

read_ris(file, encoding = "UTF-8")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references (typically empty for RIS), and keywords.

Examples

# Write a minimal RIS record to a temp file, then read it
ris <- "TY  - JOUR
AU  - Smith, J.
AU  - Jones, K.
TI  - Bibliometric networks
JO  - Test Journal
PY  - 2020
DO  - 10.1000/test
ER  - "
f <- tempfile(fileext = ".ris")
writeLines(ris, f)
data <- read_ris(f)
data[, c("id", "title", "year", "journal", "doi")]
unlink(f)

Read Scopus CSV export

Description

Parses a CSV file exported from Scopus into a standardized bibliometric data frame with list-columns for multi-valued fields.

Usage

read_scopus(file, encoding = "UTF-8")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords. Scopus-specific extras: index_keywords (list-column), affiliations (character), language (character).

Examples

f <- system.file("extdata", "scopus_sample.csv", package = "bibnets")
data <- read_scopus(f)
head(data[, c("id", "title", "year", "journal")])

Read a single bibliometric file

Description

Read a single bibliometric file

Usage

read_single_biblio(file, format, id, actors, sep, ...)

Read Web of Science plaintext or tab-delimited export

Description

Parses a Web of Science export file (plaintext or tab-delimited) into a standardized bibliometric data frame.

Usage

read_wos(file, format = "plaintext")

Arguments

Value

A data frame in the standard bibnets format: id, title, year, journal, doi, cited_by_count, abstract, type, plus list-columns authors, references, and keywords. WoS-specific extra: keywords_plus (list-column).

Examples

f <- system.file("extdata", "wos_sample.txt", package = "bibnets")
data <- read_wos(f)
head(data[, c("id", "title", "year", "journal")])

Build a reference network

Description

Constructs a co-citation or equivalence network among cited references. Two references are linked when they are cited together by the same paper.

Usage

reference_network(
  data,
  type = "co_citation",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(biblio_data)
reference_network(biblio_data)
reference_network(biblio_data, similarity = "association")

Resolve file paths from a file, vector of files, or directory

Description

Resolve file paths from a file, vector of files, or directory

Usage

resolve_paths(path)

Scopus dataset — Green Cloud Computing and Quantization (2020–2025)

Description

First 500 records from a Scopus bibliometric export on the intersection of green cloud computing and quantization, covering 2020–2025. Includes full references, author keywords, index keywords, and affiliations.

Usage

scopus_quantum_cloud

Format

A data frame with 499 rows and 12 columns:

id

Scopus EID.

title

Work title.

year

Publication year (integer).

journal

Source title.

doi

DOI string without the https://doi.org/ prefix.

cited_by_count

Times cited in Scopus.

abstract

Abstract text.

type

Document type ("Article", "Review", etc.).

authors

List-column of author name strings.

references

List-column of cited reference strings.

keywords

List-column of author keywords.

affiliations

List-column of affiliation strings.

Source

Scopus bibliometric export. Dataset archived at doi:10.5281/zenodo.17142636 (CC BY 4.0).

Examples

data(scopus_quantum_cloud)
author_network(scopus_quantum_cloud, "collaboration")
keyword_network(scopus_quantum_cloud)
document_network(scopus_quantum_cloud, "coupling", similarity = "cosine")

Build a source (journal) network

Description

Constructs a network between publication sources (journals, book series).

Usage

source_network(
  data,
  type = "coupling",
  counting = "full",
  similarity = "none",
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  self_loops = FALSE,
  deduplicate = TRUE,
  format = "edgelist"
)

Arguments

Value

Depends on format: a bibnets_network data frame (default), a Gephi-ready data frame, an igraph graph, a cograph_network, or a sparse matrix.

Examples

data(biblio_data)
source_network(biblio_data, "coupling")

Parse semicolon-delimited strings into list-column

Description

Splits semicolon-separated strings (common in Scopus/WoS exports) into character vectors, trimming whitespace.

Usage

split_field(x, sep = ";")

Arguments

Value

A list of character vectors.

Examples

split_field(c("Alice; Bob; Carol", "Dave; Eve"))

Standardize author names

Description

Uppercase, whitespace normalisation, and dot removal from initials (F.J. → FJ). Name order and format are preserved — consistent with how bibliometrix handles multi-source data.

Usage

standardize_authors(x, flip_names = FALSE)

Arguments

Value

Character vector, uppercased and cleaned.

Summarise a bibnets network

Description

Summarise a bibnets network

Usage

## S3 method for class 'bibnets_network'
summary(object, ...)

Arguments

Value

Invisibly returns object.

Examples

data(biblio_data)
edges <- author_network(biblio_data, "collaboration")
summary(edges)

Build time-windowed networks

Description

Splits data by time windows and builds a separate network for each window using any network function.

Usage

temporal_network(
  data,
  network_fun,
  ...,
  window = 3,
  step = NULL,
  strategy = "fixed",
  time_col = "year"
)

Arguments

Value

A named list of data frames (edge lists). Names are window labels like "2018-2020".

Examples

data(biblio_data)

# Fixed 3-year windows
temporal_network(biblio_data, author_network, "collaboration")

# Sliding window
temporal_network(biblio_data, author_network, "collaboration",
                 window = 2, strategy = "sliding")

# Cumulative
temporal_network(biblio_data, reference_network,
                 threshold = 0, strategy = "cumulative", window = 2)

# With string name
temporal_network(biblio_data, "keyword_network", window = 3)

Prepare network for cograph::splot()

Description

Converts a bibnets edge list to a cograph_network object by calling cograph::as_cograph(). Optionally merges node metadata (e.g., from local_citations()) into the network's node table so attributes like lcs or year can be used directly in splot() aesthetic parameters (e.g., node_size = "lcs").

Usage

to_cograph(edges, nodes = NULL, directed = FALSE)

Arguments

Details

Note: bibnets edge lists (from, to, weight) are accepted directly by cograph::splot() without conversion. This function is only needed when you want to attach node-level metadata.

Value

A cograph_network object (S3 list with ⁠$nodes⁠ and ⁠$edges⁠).

Examples

data(biblio_data)

# Without metadata: splot() accepts bibnets edges directly
edges <- author_network(biblio_data, "collaboration")

# With metadata: document network + local citation scores as node size
edges <- document_network(biblio_data, type = "coupling")
nodes <- local_citations(biblio_data)   # keyed by document id
net   <- to_cograph(edges, nodes = nodes)

Export to Gephi node and edge tables

Description

Converts a bibnets edge list (and optional node table) to the CSV format expected by Gephi's Data Laboratory. Column names are remapped to Gephi conventions (Source, Target, Weight, Id, Label).

Usage

to_gephi(edges, nodes = NULL, file = NULL, directed = FALSE)

Arguments

Value

If file = NULL: a list with ⁠$nodes⁠ and ⁠$edges⁠ data frames. If file is a directory path: writes two CSV files invisibly and returns the file paths.

Examples

data(biblio_data)
edges <- author_network(biblio_data, "collaboration")
gephi <- to_gephi(edges)
head(gephi$edges)

Export to GraphML

Description

Writes a bibnets edge list (and optional node attributes) to a GraphML file using pure base R — no XML package required.

Usage

to_graphml(edges, nodes = NULL, file = NULL, directed = FALSE)

Arguments

Value

If file = NULL: GraphML as a character string. Otherwise writes the file and returns the path invisibly.

Examples

data(biblio_data)
edges <- keyword_network(biblio_data)
xml <- to_graphml(edges)
cat(substr(xml, 1, 300))

Convert edge data frame to igraph

Description

Convert edge data frame to igraph

Usage

to_igraph(edges, directed = FALSE)

Arguments

Value

An igraph graph object.

Examples

data(biblio_data)
edges <- author_network(biblio_data, "collaboration")
g <- to_igraph(edges)

Convert edge data frame to adjacency matrix

Description

Convert edge data frame to adjacency matrix

Usage

to_matrix(edges, symmetric = TRUE)

Arguments

Value

A sparse Matrix.

Examples

data(biblio_data)
edges <- reference_network(biblio_data, min_occur = 2)
to_matrix(edges)

Convert edge data frame to tbl_graph

Description

Convert edge data frame to tbl_graph

Usage

to_tbl_graph(edges, directed = FALSE)

Arguments

Value

A tbl_graph object (tidygraph).

Examples

data(biblio_data)
edges <- keyword_network(biblio_data)
tg <- to_tbl_graph(edges)