MC_baseline_distribution

Graph file (created with build_graph)

List of the names of the two corpora studied (e.g. c("Computer Science", "Mathematics")), present in the "Corpus" attribute

labels

Minimum year considered in this study

infYearLimit

Maximum year considered in this study

supYearLimit

This function performs Monte Carlo runs with random permutations of corpus tags in the graph provided and computes the heterocitation balance on the new graphs. Permutation is repeated over several iterations (set through the "rep" argument) and provides a baseline Dx values for the graph topology considered. This can then be compared with the Dx value obtained for the original graph to evaluate whether it could merely be the result of chance (see <code><a rd-options="" href="/link/significance_Dx?package=Diderot&version=0.13" data-mini-rdoc="Diderot::significance_Dx">significance_Dx</a></code>).

Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.

Christian Vincenot

Diderot

Bibliographic Network Analysis

MC_baseline_distribution function

This function performs Monte Carlo runs with random permutations of corpus tags in the graph provided and computes the heterocitation balance on the new graphs. Permutation is repeated over several iterations (set through the "rep" argument) and provides a baseline Dx values for the graph topology considered. This can then be compared with the Dx value obtained for the original graph to evaluate whether it could merely be the result of chance (see <code><a rd-options='' href='significance_Dx'>significance_Dx</a></code>).

MC_baseline_distribution: Function to compute baseline heterocitation values for the graph under study with random permutation of corpus attributions

Description

Usage

Arguments

Value

See Also