Bibliographic coupling edges, with the weighting method of your choice

The general entry point to bibliographic coupling. From a table of direct citations (one row per citing document and cited reference), it returns the edges of the network linking the documents that share references (Kessler 1963) , weighted by the method you choose. It is a thin wrapper: every method is also available as a function of its own, which documents the formula and the reasons to choose it.

`method`	Function	Weight
`"angle"` (default)	`coupling_angle()`	cosine of the bibliographies (Sen and Gan 1983)
`"strength"`	`coupling_strength()`	shared references weighted by their rarity (Shen et al. 2019)
`"similarity"`	`coupling_similarity()`	idf-weighted cosine (Shen et al. 2019)
`"jaccard"`	`coupling_jaccard()`	shared references over the union of the bibliographies (Sen and Gan 1983)
`"vladutz"`	`coupling_vladutz()`	Vladutz and Cook formula (Vladutz and Cook 1984)

In versions up to 0.1.1, biblio_coupling() was what is now coupling_angle(): calls that leave method to its default give the same result. To compare several methods at once on the same corpus, use compute_couplings(); to couple authors, journals or institutions rather than documents, use entity_network(); to link cited references, use biblio_cocitation().

Usage

biblio_coupling(
  dt,
  source,
  ref,
  method = c("angle", "strength", "similarity", "jaccard", "vladutz"),
  normalized_weight_only = TRUE,
  weight_threshold = 1,
  output_in_character = TRUE
)

Arguments

dt: A data frame or a data.table of direct citations, one row per link between a citing document and a cited reference. It is not modified.
source: Character scalar. The column of the citing documents: they are the nodes of the coupling network.
ref: Character scalar. The column of the cited references.
method: The weighting method, one of "angle" (default), "strength", "similarity", "jaccard" and "vladutz". See the table above and the help page of each function.
normalized_weight_only: Logical. If FALSE, the number of shared references is returned in addition to the normalised weight.
weight_threshold: Number. The minimum number of shared references for two documents to be linked (1 by default: any shared reference creates a tie). A threshold of 2 or 3 is common in large corpora, where sharing a single reference is weak evidence of proximity and produces very dense networks. It applies to the number of shared references, not to the normalised weight.
output_in_character: Logical. If TRUE (default), the identifiers are also returned as character columns from and to, to build a tidygraph graph more easily.

Value

A data.table with one row per pair of documents sharing at least weight_threshold references: Source and Target (identifiers, with Source < Target), from and to (the same, as characters, if output_in_character = TRUE), the weight and, if normalized_weight_only = FALSE, nb_shared_references. Whatever the method, the weights of different methods do not have the same scale: see compute_couplings() to compare them.

Details

References

Kessler MM (1963). “Bibliographic Coupling between Scientific Papers.” American Documentation, 14(1), 10–25. doi:10.1002/asi.5090140103 .

Sen SK, Gan SK (1983). “A Mathematical Extension of the Idea of Bibliographic Coupling and Its Applications.” Annals of library science and documentation, 30(2).

Shen S, Zhu D, Rousseau R, Su X, Wang D (2019). “A Refined Method for Computing Bibliographic Coupling Strengths.” Journal of Informetrics, 13(2), 605–615. https://linkinghub.elsevier.com/retrieve/pii/S1751157716300244.

Vladutz G, Cook J (1984). “Bibliographic Coupling and Subject Relatedness.” Proceedings of the American Society for Information Science, 21, 204–207.

Examples

library(biblionetwork)
# The default is the coupling angle...
head(biblio_coupling(Ref_stagflation,
                     source = "Citing_ItemID_Ref",
                     ref = "ItemID_Ref",
                     weight_threshold = 3))
#>       from       to     weight  Source   Target
#>     <char>   <char>      <num>   <int>    <int>
#> 1:  214927  2207578 0.14605935  214927  2207578
#> 2: 5160307  6714008 0.09128709 5160307  6714008
#> 3: 1717556  7815498 0.16564729 1717556  7815498
#> 4:  214927  8456979 0.09733285  214927  8456979
#> 5: 2207578  8456979 0.11846978 2207578  8456979
#> 6: 5240516 10585974 0.14726421 5240516 10585974

# ...and the method changes the weights, not the pairs of documents
head(biblio_coupling(Ref_stagflation,
                     source = "Citing_ItemID_Ref",
                     ref = "ItemID_Ref",
                     method = "strength",
                     weight_threshold = 3))
#>       from       to     weight  Source   Target
#>     <char>   <char>      <num>   <int>    <int>
#> 1:  214927  2207578 0.02000862  214927  2207578
#> 2: 5160307  6714008 0.01061175 5160307  6714008
#> 3: 1717556  7815498 0.03526234 1717556  7815498
#> 4:  214927  8456979 0.01193990  214927  8456979
#> 5: 2207578  8456979 0.01280790 2207578  8456979
#> 6: 5240516 10585974 0.01146359 5240516 10585974