Build an institution network — institution

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

Arguments

data

A data frame with id and an affiliation column (list-column or delimited string). For coupling, also needs references.

type

Character. "collaboration" (default), "coupling", or "equivalence".

counting

Character. Counting method. Default "full".

similarity

Character. Similarity measure. Default "none".

threshold

Numeric. Minimum edge weight. Default 0.

min_occur

Integer. Minimum papers per institution. 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.

affiliations

Character. Name of the column containing institutions. Default "affiliations".

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 ";".

strip_quotes

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

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(learning_analytics)
institution_network(learning_analytics, "collaboration")
#> # bibnets network: institution_collaboration | 1,199 nodes · 2,649 edges | counting: full 
#>     from                            to                              weight  count
#>  1  FINLAND UNIVERSITY              UNIVERSITY OF EASTERN FINLAND       13     13
#>  2  DIPF                            LEIBNIZ INSTITUTE FOR RESEARC…      10     10
#>  3  UNIVERSIDADE FEDERAL DE SANTA…  UNIVERSITY OF VALPARAÍSO           10     10
#>  4  MAASTRICHT SCHOOL OF MANAGEME…  MAASTRICHT UNIVERSITY                6      6
#>  5  ESCUELA SUPERIOR POLITECNICA …  MONASH UNIVERSITY                    6      6
#>  6  PONTIFICIA UNIVERSIDAD CATÓLI…  UNIVERSIDADE FEDERAL DE SANTA…       6      6
#>  7  UNIVERSITY OF EASTERN FINLAND   UNIVERSITY OF JYVÄSKYLÄ            6      6
#>  8  PONTIFICIA UNIVERSIDAD CATÓLI…  UNIVERSITY OF VALPARAÍSO            6      6
#>  9  DIPF                            GOETHE UNIVERSITY FRANKFURT          5      5
#> 10  GOETHE UNIVERSITY FRANKFURT     LEIBNIZ INSTITUTE FOR RESEARC…       5      5
#> # ... 2,639 more edges