Build a co-occurrence network from any field

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",
  strip_quotes = TRUE,
  id = NULL
)

Arguments

data

A data frame with column id and the specified field(s).

field

Character. The entity field — determines what the nodes are.

by

Character or NULL. What links the nodes. If NULL (default), entities are linked by co-occurring in the same document. If specified, entities are linked when they share values from the by field.

sep

Character or NULL. Delimiter for splitting character columns. Default ";". Set to NULL if columns are already list-columns.

counting

Character. Counting method. Default "full".

similarity

Character. Normalization method. Default "none".

threshold

Numeric. Minimum edge weight. Default 0.

min_occur

Integer. Minimum entity frequency. Default 1.

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.

strip_quotes

Logical. If TRUE (default), surrounding quote characters are removed from each entity, so a quoted CSV value such as "Alice" or ""Alice"" is treated as Alice. Set FALSE to keep quotes as part of the label.

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.

Details

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

Examples

data(biblio_data)

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

# Authors linked by shared keywords
conetwork(biblio_data, "authors", by = "keywords")
#> # bibnets network: authors_by_keywords | 6 nodes · 15 edges | counting: full 
#>     from     to       weight  count
#>  1  CHEN W   LEE K         8      8
#>  2  BROWN M  SMITH J       8      8
#>  3  BROWN M  LEE K         6      6
#>  4  JONES A  LEE K         6      6
#>  5  JONES A  SMITH J       6      6
#>  6  LEE K    SMITH J       6      6
#>  7  DAVIS R  JONES A       4      4
#>  8  BROWN M  CHEN W        3      3
#>  9  BROWN M  DAVIS R       3      3
#> 10  CHEN W   DAVIS R       3      3
#> # ... 5 more edges

# Keywords linked by shared authors
conetwork(biblio_data, "keywords", by = "authors")
#> # bibnets network: keywords_by_authors | 23 nodes · 190 edges | counting: full 
#>     from           to                   weight  count
#>  1  BIBLIOMETRICS  CO-OCCURRENCE             4      4
#>  2  BIBLIOMETRICS  NETWORK ANALYSIS          4      4
#>  3  CO-OCCURRENCE  NETWORK ANALYSIS          4      4
#>  4  BIBLIOMETRICS  NORMALIZATION             4      4
#>  5  BIBLIOMETRICS  CO-CITATION               3      3
#>  6  CO-CITATION    CO-OCCURRENCE             3      3
#>  7  BIBLIOMETRICS  FRACTIONAL COUNTING       3      3
#>  8  BIBLIOMETRICS  KEYWORD MAPPING           3      3
#>  9  CO-OCCURRENCE  KEYWORD MAPPING           3      3
#> 10  BIBLIOMETRICS  KNOWLEDGE DOMAINS         3      3
#> # ... 180 more edges

# Journals linked by shared references (= journal coupling)
conetwork(biblio_data, "journal", by = "references", similarity = "cosine")
#> # bibnets network: journal_by_references | 4 nodes · 6 edges | counting: full | similarity: cosine 
#>    from                          to                            weight  count
#> 1  JASIST                        SCIENTOMETRICS                0.8018      6
#> 2  JASIST                        JOURNAL OF INFORMETRICS       0.7071      6
#> 3  JOURNAL OF INFORMETRICS       SCIENTOMETRICS                0.6299      5
#> 4  JOURNAL OF INFORMETRICS       QUANTITATIVE SCIENCE STUDIES  0.3333      2
#> 5  QUANTITATIVE SCIENCE STUDIES  SCIENTOMETRICS                 0.189      1
#> 6  JASIST                        QUANTITATIVE SCIENCE STUDIES  0.1768      1

# Auto-splits semicolon-delimited string columns
d <- data.frame(id = 1:3, tags = c("ml; dl; nlp", "ml; cv", "dl; cv"))
conetwork(d, "tags")
#> # bibnets network: tags_co_occurrence | 4 nodes · 5 edges | counting: full 
#>    from  to   weight  count
#> 1  CV    DL        1      1
#> 2  CV    ML        1      1
#> 3  DL    ML        1      1
#> 4  DL    NLP       1      1
#> 5  ML    NLP       1      1