`R/biblio_cocitation.R`

`biblio_cocitation.Rd`

This function is basically the same as the `biblio_coupling()`

function but it is explicitly framed
for bibliographic co-citation network (and not for bibliographic coupling networks). It takes a data frame
with direct citations, and calculates the number of times two references are citing together, and calculate a measure
similar to the coupling angle value (Sen and Gan 1983)
: it divides the number of times two references are
cited together by the square root of the product of the total number of citations (in the whole corpus) of each reference.
The more two references are cited in general, the more they have to be cited together for their link to be important.

biblio_cocitation( dt, source, ref, normalized_weight_only = TRUE, weight_threshold = 1, output_in_character = TRUE )

dt | The dataframe with citing and cited documents. |
---|---|

source | The column name of the source identifiers, that is the documents that are citing. |

ref | The column name of the cited references identifiers. In co-citation network, these references are the nodes of the network. |

normalized_weight_only | If set to FALSE, the function returns the weights normalized by the cosine measure, but also simply the number of times two references are cited together. |

weight_threshold | Correspond to the value of the non-normalized weights of edges. The function just keeps the edges
that have a non-normalized weight superior to the |

output_in_character | If TRUE, the function ends by transforming the |

A data.table with the articles (or authors) identifier in `from`

and `to`

columns,
with one or two additional columns (the coupling angle measure and
the number of shared references). It also keeps a copy of `from`

and `to`

in the `Source`

and `Target`

columns. This is useful is you
are using the tidygraph package then, where `from`

and `to`

values are modified when creating a graph.

This function uses data.table package and is thus very fast. It allows the user to compute the coupling angle on a very large data frame quickly.

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).
http://nopr.niscair.res.in/bitstream/123456789/28008/1/ALIS%2030(2)%2078-82.pdf.

library(biblionetwork) biblio_cocitation(Ref_stagflation, source = "Citing_ItemID_Ref", ref = "ItemID_Ref")#> from to weight Source Target #> 1: 49248 180162 1.0000000 49248 180162 #> 2: 49248 804988 0.3162278 49248 804988 #> 3: 49248 1999903 1.0000000 49248 1999903 #> 4: 49248 2031010 1.0000000 49248 2031010 #> 5: 49248 3580645 0.7071068 49248 3580645 #> --- #> 87664: 1111112223 1111112225 1.0000000 1111112223 1111112225 #> 87665: 1111112223 1111112227 1.0000000 1111112223 1111112227 #> 87666: 1111112224 1111112225 1.0000000 1111112224 1111112225 #> 87667: 1111112224 1111112227 1.0000000 1111112224 1111112227 #> 87668: 1111112225 1111112227 1.0000000 1111112225 1111112227# It is basically the same as: biblio_coupling(Ref_stagflation, source = "ItemID_Ref", ref = "Citing_ItemID_Ref")#> from to weight Source Target #> 1: 49248 180162 1.0000000 49248 180162 #> 2: 49248 804988 0.3162278 49248 804988 #> 3: 49248 1999903 1.0000000 49248 1999903 #> 4: 49248 2031010 1.0000000 49248 2031010 #> 5: 49248 3580645 0.7071068 49248 3580645 #> --- #> 87664: 1111112223 1111112225 1.0000000 1111112223 1111112225 #> 87665: 1111112223 1111112227 1.0000000 1111112223 1111112227 #> 87666: 1111112224 1111112225 1.0000000 1111112224 1111112225 #> 87667: 1111112224 1111112227 1.0000000 1111112224 1111112227 #> 87668: 1111112225 1111112227 1.0000000 1111112225 1111112227