Compute several coupling (or co-citation) measures at once

Computes several measures of bibliographic coupling (or of co-citation) on the same table of citations and returns a named list of edge tables, ready for summarise_weights(), correlate_weights(), overlap_top_weights(), merge_weights() and plot_weights(). The costly step (finding all the pairs of nodes that share a reference) is done only once, so computing the five measures costs little more than computing one.

Usage

compute_couplings(
  dt,
  source,
  ref,
  methods = c("angle", "strength", "similarity", "jaccard", "vladutz"),
  weight_threshold = 1,
  network = c("coupling", "cocitation"),
  title = NULL,
  descriptive_args = list()
)

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.
methods: Character vector, among "angle" (coupling_angle()), "strength" (coupling_strength()), "similarity" (coupling_similarity()), "jaccard" (coupling_jaccard()), "vladutz" (coupling_vladutz()) and "descriptive" (coupling_descriptive()). The first five are computed by default; "descriptive" is not, since it is much more costly (see the details), and it needs title.
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.
network: "coupling" (default): the nodes are the documents of source. "cocitation": the nodes are the references of ref, and the measures are those of biblio_cocitation().
title: Character scalar. The column with the title of the cited references, needed (only) for methods including "descriptive".
descriptive_args: A list of further arguments for coupling_descriptive() (targets, stopwords, min_weight, max_documents).

Value

A named list with one data.table per method, each with columns Source, Target and weight. The tables of the five measures describe the same pairs of nodes (those sharing at least weight_threshold references, or co-cited at least weight_threshold times), in the same order. The table of "descriptive" has its own pairs.

Details

The five measures that start from the shared references are computed on the same pairs of nodes (those sharing at least weight_threshold references), so that their weights can be compared pair by pair. "descriptive" (the DescriptiveBC measure of (Liu 2017) ) is different in two ways. It couples documents that cite different but related references, so its pairs are not the same ones: any pair of documents with similar titles of references is concerned, whatever the number of shared references (the weight_threshold does not apply to it). And its cost grows with the square of the number of documents: the function informs you when you ask for it, and you should give targets or work on a time window for a large corpus. merge_weights() puts the tables side by side, with NA for the pairs a measure does not have.

Why use it in research

The choice of a measure is a modelling choice, not a technical detail: it determines which pairs of documents look close, and thus the communities found. (Shen et al. 2019) show on one corpus that the measures can be very close (coupling angle and similarity) or quite different (Vladutz-Cook and the others). Whether it matters for your corpus can only be checked on your corpus: compute the measures with this function, and compare the distributions and the rankings of the weights. The article comparing the measures does it on the stagflation corpus.

References

Liu R (2017). “A New Bibliographic Coupling Measure with Descriptive Capability.” Scientometrics, 110(2), 915–935. doi:10.1007/s11192-016-2196-7 .

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.

Examples

library(biblionetwork)
edges <- compute_couplings(Ref_stagflation,
                           source = "Citing_ItemID_Ref",
                           ref = "ItemID_Ref",
                           weight_threshold = 2)
names(edges)
#> [1] "angle"      "strength"   "similarity" "jaccard"    "vladutz"   
summarise_weights(edges)
#>        method n_edges        mean         sd          min         q01
#>        <char>   <int>       <num>      <num>        <num>       <num>
#> 1:      angle    1501 0.107669186 0.06044895 0.0207223291 0.030740141
#> 2:   strength    1501 0.009367874 0.01299973 0.0005415795 0.001160528
#> 3: similarity    1501 0.070717811 0.04753687 0.0097282985 0.016752491
#> 4:    jaccard    1501 0.053513766 0.03310502 0.0103092784 0.014925373
#> 5:    vladutz    1501 0.022122321 0.02291125 0.0016339869 0.002849544
#>            q25        q50        q75        q99       max share_zero
#>          <num>      <num>      <num>      <num>     <num>      <num>
#> 1: 0.064051262 0.09274778 0.13483997 0.31250000 0.4291975          0
#> 2: 0.003600744 0.00606473 0.01070624 0.05099059 0.2537136          0
#> 3: 0.038402090 0.05799043 0.08680766 0.24852872 0.3578691          0
#> 4: 0.030612245 0.04444444 0.06756757 0.17647059 0.2692308          0
#> 5: 0.008720930 0.01487280 0.02681704 0.11333333 0.2230769          0
#>    ratio_q99_median
#>               <num>
#> 1:         3.369353
#> 2:         8.407726
#> 3:         4.285685
#> 4:         3.970588
#> 5:         7.620175
correlate_weights(edges)
#>                angle  strength similarity   jaccard   vladutz
#> angle      1.0000000 0.8523666  0.9592276 0.9802158 0.6866473
#> strength   0.8523666 1.0000000  0.9005974 0.8121718 0.7284654
#> similarity 0.9592276 0.9005974  1.0000000 0.9375955 0.8410196
#> jaccard    0.9802158 0.8121718  0.9375955 1.0000000 0.7018655
#> vladutz    0.6866473 0.7284654  0.8410196 0.7018655 1.0000000
#> attr(,"n_pairs")
#> [1] 1501

# The titles-based measure is not computed by default; it needs the titles of the references
# \donttest{
toy <- data.frame(
  doc = c("a", "a", "b", "b"),
  ref = c("r1", "r2", "r1", "r3"),
  title = c("inflation expectations", "oil supply shocks",
            "inflation expectations", "supply shocks prices")
)
compute_couplings(toy, "doc", "ref", methods = c("angle", "descriptive"), title = "title")
#> ℹ Computing the "descriptive" measure of Liu (2017), which is not part of the
#>   default measures.
#> ! Its cost grows with the square of the number of documents, and its pairs are
#>   not those of the other measures: documents with no shared reference can be
#>   linked.
#> ℹ For a large corpus, give `descriptive_args = list(targets = ...)` or work by
#>   time window.
#> $angle
#>    Source Target weight
#>    <char> <char>  <num>
#> 1:      a      b    0.5
#> 
#> $descriptive
#>    Source Target weight
#>    <char> <char>  <num>
#> 1:      a      b   0.75
#> 
# }