bibliometrix (version 1.9)

thematicEvolution: Create an Evolution Thematic Map

Description

It creates an Evolution thematic map based on co-word network analysis and clustering. The methodology is inspired by the proposal of Cobo et al. (2011).

Usage

thematicEvolution(..., weighted = FALSE)

Arguments

...

is a sequence of names of thematic maps created by thematicMap function.

weighted

is a logical. If FALSE, a thematic nexus is measures by the classical inclusion index (calculated using the number of keywords). If TRUE, the inclusion index is calculated considering the occurrences of keywords.

Value

a list containing:

nets

The thematic nexus graph for each comparison

Details

thematicEvolution starts from two or more thematic maps created by thematicMap function.

See Also

thematicMap function to create a thematic map based on co-word network analysis and clustering.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

Examples

# NOT RUN {
data(scientometrics)
years=c(2000)
list_df=timeslice(scientometrics, breaks = years)
M1=list_df[[1]]
M2=list_df[[2]]
NetMatrix1 <- biblioNetwork(M1, analysis = "co-occurrences", 
              network = "keywords", sep = ";")
S1 <- normalizeSimilarity(NetMatrix1, type = "association")
net1 <- networkPlot(S1, n = 50, Title = "co-occurrence network",type="fruchterman",
     labelsize = 0.7, halo = FALSE, cluster = "walktrap",remove.isolates=FALSE,
     remove.multiple=FALSE, noloops=TRUE, weighted=TRUE)
res1 <- thematicMap(net1, NetMatrix1, S1)
#plot(res1$map)

NetMatrix2 <- biblioNetwork(M2, analysis = "co-occurrences", 
              network = "keywords", sep = ";")
S2 <- normalizeSimilarity(NetMatrix2, type = "association")
net2 <- networkPlot(S2, n = 50, Title = "co-occurrence network",type="fruchterman",
     labelsize = 0.7, halo = FALSE, cluster = "walktrap",remove.isolates=FALSE,
     remove.multiple=FALSE, noloops=TRUE, weighted=TRUE)
res2 <- thematicMap(net2, NetMatrix2, S2)
#plot(res2$map)

nexus <- thematicEvolution(res1,res2,weighted=FALSE)

# }