heterocitation {Diderot}

Function to calculate the heterocitation between two corpora

Description

This function calculates the heterocitation share and heterocitation balance between two corpora A and B in the time window specified. The heterocitation share (Sx) of a publication belonging to corpus A is defined as the percentage of citations to publications belonging to corpus B (or A|B) in its reference list. The global heterocitation share for corpus A is calculated as the average heterocitation share of the publications that corpus A contains (e.g. a value of 0.2 for corpus A indicates that, on average, publications in corpus A cite only 20% of papers from corpus B). The heterocitation balance metric (Dx), on the other hand, takes into consideration the respective sizes of corpus A and B to discern how much the heterocitation share deviates from values expected in the case of well-mixedness (i.e. if A and B originated from a unique community; e.g. a value of -50% for corpus A indicates that, on average, publications in corpus A cite papers from corpus B half less frequently than expected, which suggests a lack of mutual awareness between the corpora and related communities).

Usage

heterocitation(gr, labels, infLimitYear, supLimitYear)

Arguments

gr

Citation graph priorly preprocessed with precompute_heterocitation

labels

Labels (i.e. names) of the two corpora featured in the graph.

infLimitYear

Start year of the time window considered (included)

supLimitYear

End year of the time window considered (*excluded*)

Value

Returns a numerical vector containing, in this order, the heterocitation share (Sx) for corpus A, B and global, and the heterocitation balance (Dx) for A, B and global.

Note

precompute_heterocitation should be called before running this function.

Author(s)

Christian Vincenot ([e-mail])

See Also

precompute_heterocitation, plot_heterocitation_timeseries, heterocitation_authors, MC_baseline_distribution, significance_Dx

Examples

labels<-c("Corpus1","Corpus2")

# Build a bibliographical dataset from Scopus exports
db<-create_bibliography(corpora_files=c(tempfi1,tempfi2), 
                        labels=labels, keywords=NA)


# Build graph
gr<-build_graph(db=db,small.year.mismatch=TRUE, attrs=c("Corpus","Year","Authors"), nb.cores=1)

# Heterocitation
gr<-precompute_heterocitation(gr,labels, 1990, 2018)
heterocitation(gr,labels, 1990, 2018)