Build a co-word (co-occurrence) network

Constructs an undirected co-word network. Nodes represent terms and an edge connects two terms if they co-occur within the same work. Edge weight equals the number of works in which both terms appear.

Usage

sm_network_coword(
  corpus,
  field = c("concepts", "title", "abstract", "keywords"),
  ngram = 1L,
  min_freq = 5L,
  stopwords = NULL,
  call = rlang::caller_env()
)

Arguments

corpus

An sm_corpus object.

field

Character; the text field to extract terms from. One of "concepts" (default, uses corpus$concepts), "title", "abstract", or "keywords".

ngram

Integer; for free-text fields ("title", "abstract"), the n-gram size to tokenise into. Defaults to 1L (unigrams).

min_freq

Integer; minimum document frequency a term must have to be included. Defaults to 5L.

stopwords

Character vector of additional stopwords to remove when tokenising free text. Combined with tidytext::stop_words. Set to NULL (default) for no additional stopwords.

call

Caller environment for error reporting.

Value

A tidygraph::tbl_graph object (undirected). Nodes carry name (the term) and freq (document frequency). Edges carry a weight column (co-occurrence count).

Details

When field = "concepts", terms come from the concept_name column in corpus$concepts (no tokenisation needed).

When field is "title", "abstract", or "keywords", the text is tokenised with tidytext::unnest_tokens() using the chosen ngram size, stopwords are removed, and terms below min_freq are dropped.

Empty input returns an empty undirected tbl_graph.

See also

Other networks: sm_network_citation(), sm_network_cocitation(), sm_network_collab(), sm_network_coupling(), sm_network_semantic()

Examples

corpus <- sm_example_corpus()
g <- sm_network_coword(corpus, field = "concepts", min_freq = 5L)
g
#> # A tbl_graph: 10 nodes and 45 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 10 × 2 (active)
#>    name                     freq
#>    <chr>                   <int>
#>  1 biomarker discovery        74
#>  2 clinical outcomes          72
#>  3 colorectal cancer          82
#>  4 drug resistance            76
#>  5 gene expression            57
#>  6 immune checkpoint          79
#>  7 machine learning           66
#>  8 single-cell rna-seq        74
#>  9 spatial transcriptomics    67
#> 10 tumor microenvironment     58
#> #
#> # Edge Data: 45 × 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1     2     21
#> 2     1     3     26
#> 3     1     4     30
#> # ℹ 42 more rows