Build an author network — author

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",
  authors = "authors",
  sep = ";",
  references_sep = ";",
  strip_quotes = TRUE,
  id = NULL
)

Arguments

data

A data frame with at least id and an author column (list-column or delimited string, order preserved). For coupling/co-citation, also needs references.

type

Character. Relationship type:

"collaboration": Co-authorship: authors linked when they co-author a publication.
"coupling": Bibliographic coupling aggregated at author level: authors linked when they cite the same references.
"co_citation": Author co-citation: authors linked when they are cited together by the same paper. Requires a cited_first_authors list-column.
"equivalence": Profile similarity: cosine similarity of authors' full collaboration/citation profiles.

counting

Character. Counting method. Position-independent methods ("full", "fractional", "paper", "strength") work for all types. Position-dependent methods ("harmonic", "arithmetic", "geometric", "adaptive_geometric", "golden", "first", "last", "first_last", "position_weighted") are available for type = "collaboration".

similarity

Character. Similarity measure: "none", "association", "cosine", "jaccard", "inclusion", "equivalence".

threshold

Numeric. Minimum edge weight. Default 0.

min_occur

Integer. Minimum number of papers for an author to be included. Default 1.

position_weights

Numeric vector. Custom weights for counting = "position_weighted". Default c(1, 0.8, 0.6, 0.4).

first_last_weight

Numeric. Multiplier for counting = "first_last". Default 2.

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.

authors

Character. Name of the column containing authors. Default "authors". Use this to point at any column of a custom data set, e.g. authors = "Author Names".

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.

references_sep

Character. Separator for the references column in type = "coupling". Default ";" (reference strings usually contain internal commas, so this is kept independent of sep). Set it when your references are delimited differently.

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.

Examples

data(biblio_data)
author_network(biblio_data, "collaboration")
#> # bibnets network: author_collaboration | 6 nodes · 12 edges | counting: full 
#>     from     to       weight  count
#>  1  CHEN W   LEE K         3      3
#>  2  BROWN M  SMITH J       3      3
#>  3  BROWN M  LEE K         2      2
#>  4  JONES A  LEE K         2      2
#>  5  JONES A  SMITH J       2      2
#>  6  LEE K    SMITH J       2      2
#>  7  BROWN M  CHEN W        1      1
#>  8  BROWN M  DAVIS R       1      1
#>  9  CHEN W   DAVIS R       1      1
#> 10  CHEN W   JONES A       1      1
#> # ... 2 more edges
author_network(biblio_data, "collaboration", counting = "harmonic")
#> # bibnets network: author_collaboration | 6 nodes · 12 edges | counting: harmonic 
#>     from     to       weight  count
#>  1  BROWN M  SMITH J  0.4702      3
#>  2  JONES A  SMITH J   0.371      2
#>  3  CHEN W   LEE K    0.3214      3
#>  4  CHEN W   DAVIS R  0.2222      1
#>  5  DAVIS R  JONES A  0.2222      1
#>  6  JONES A  LEE K    0.1983      2
#>  7  LEE K    SMITH J  0.1983      2
#>  8  BROWN M  CHEN W   0.1488      1
#>  9  BROWN M  DAVIS R  0.1488      1
#> 10  BROWN M  LEE K    0.1488      2
#> # ... 2 more edges
author_network(biblio_data, "collaboration", counting = "geometric",
               similarity = "association")
#> # bibnets network: author_collaboration | 6 nodes · 12 edges | counting: geometric | similarity: association 
#>     from     to       weight  count
#>  1  CHEN W   LEE K    0.7868      3
#>  2  CHEN W   DAVIS R  0.5303      1
#>  3  BROWN M  SMITH J  0.4494      3
#>  4  DAVIS R  JONES A  0.3619      1
#>  5  JONES A  LEE K    0.3606      2
#>  6  JONES A  SMITH J  0.3255      2
#>  7  BROWN M  DAVIS R  0.3029      1
#>  8  BROWN M  CHEN W   0.2935      1
#>  9  BROWN M  LEE K    0.2465      2
#> 10  LEE K    SMITH J  0.2262      2
#> # ... 2 more edges

# Custom CSV: any column name, any separator
d <- data.frame(id = 1:3,
                Researchers = c("Smith J, Doe A", "Smith J, Lee K",
                                "Doe A, Lee K"))
author_network(d, authors = "Researchers", sep = ",")
#> # bibnets network: author_collaboration | 3 nodes · 3 edges | counting: full 
#>    from   to       weight  count
#> 1  DOE A  LEE K         1      1
#> 2  DOE A  SMITH J       1      1
#> 3  LEE K  SMITH J       1      1