Build a keyword co-occurrence network

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

Usage

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

Arguments

data

A data frame with id and a keyword list-column.

keywords

Character. Name of the keyword column (list-column or delimited string). Default "keywords". Any column of a custom data set works, e.g. keywords = "Tags".

counting

Character. Counting method. Default "full".

similarity

Character. Similarity measure. Default "none".

threshold

Numeric. Minimum edge weight. Default 0.

min_occur

Integer. Minimum keyword frequency. Default 1.

attention

Character or NULL. Attention-based weighting independent of type and counting. One of "proximity" (center authors weighted most), "lead" (first author dominates, quadratic drop), "last" (last author dominates, quadratic rise), "circular" (first and last both prominent). Default NULL (disabled).

top_n

Integer or NULL. Return only the top n edges by weight. Default NULL (all edges).

self_loops

Logical. If TRUE, include self-loops (an entity linked to itself). Default FALSE.

deduplicate

Logical. If TRUE (default), each (paper, entity) pair is counted at most once — duplicate entries in the source data (e.g., the same author listed twice on a paper) are treated as one occurrence. Set to FALSE to count every raw occurrence.

format

Character. Output format:

"edgelist"

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

"gephi"

Gephi-ready data frame: Source, Target, Weight, Count, Type.

"igraph"

An igraph graph object (requires igraph).

"cograph"

A cograph_network object (requires cograph).

"matrix"

A sparse adjacency matrix.

sep

Character. Separator used to split the entity column when it is a plain character column rather than a list-column, e.g. sep = "," or sep = " and ". Default ";". Ignored for list-columns. sep applies only to the author column; the references column uses references_sep.

strip_quotes

Logical. If TRUE (default), surrounding quote characters are removed from each keyword.

field

Deprecated. Use keywords instead.

id

Optional. Name of the column to use as the work identifier (the matrix-row dimension). If NULL (default), an existing id column is used when present, otherwise row numbers are used.

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)
#> # bibnets network: keyword_co_occurrence | 23 nodes · 30 edges | counting: full 
#>     from               to                   weight  count
#>  1  CITATION NETWORKS  CLUSTERING                1      1
#>  2  BIBLIOMETRICS      CO-CITATION               1      1
#>  3  BIBLIOMETRICS      CO-OCCURRENCE             1      1
#>  4  CITATION NETWORKS  COMMUNITY DETECTION       1      1
#>  5  CLUSTERING         COMMUNITY DETECTION       1      1
#>  6  BIBLIOMETRICS      COUPLING                  1      1
#>  7  AUTHOR NAMES       DISAMBIGUATION            1      1
#>  8  CITATION PATTERNS  DYNAMICS                  1      1
#>  9  AUTHOR NAMES       ENTITY RESOLUTION         1      1
#> 10  DISAMBIGUATION     ENTITY RESOLUTION         1      1
#> # ... 20 more edges
keyword_network(biblio_data, similarity = "association")
#> # bibnets network: keyword_co_occurrence | 23 nodes · 30 edges | counting: full | similarity: association 
#>     from               to                       weight  count
#>  1  CITATION NETWORKS  CLUSTERING                    1      1
#>  2  CITATION NETWORKS  COMMUNITY DETECTION           1      1
#>  3  CLUSTERING         COMMUNITY DETECTION           1      1
#>  4  AUTHOR NAMES       DISAMBIGUATION                1      1
#>  5  CITATION PATTERNS  DYNAMICS                      1      1
#>  6  AUTHOR NAMES       ENTITY RESOLUTION             1      1
#>  7  DISAMBIGUATION     ENTITY RESOLUTION             1      1
#>  8  COUPLING           RESEARCH FRONTS               1      1
#>  9  CO-AUTHORSHIP      SCHOLARLY COMMUNICATION       1      1
#> 10  KNOWLEDGE DOMAINS  SCIENCE MAPPING               1      1
#> # ... 20 more edges

# Custom CSV: any column name, any separator
d <- data.frame(id = 1:3, Tags = c("ml, ai", "ml, nlp", "ai, nlp"))
keyword_network(d, keywords = "Tags", sep = ",")
#> # bibnets network: keyword_co_occurrence | 3 nodes · 3 edges | counting: full 
#>    from  to   weight  count
#> 1  AI    ML        1      1
#> 2  AI    NLP       1      1
#> 3  ML    NLP       1      1