setwd("...") #insert your work directory

library ("igraph") #for network analysis
library(ade4) #for jaccard or other normalizations

# Load matrix from csv
M = as.matrix(read.csv(file.choose(), sep = ";", header = T, row.names = 1, check.names=FALSE))

#Transform into an unweighted, directed, twomode igraph object (Jaccard works best with unweighted/ binary matrices)
two_mode_network <- graph.incidence(M, directed = TRUE, mode = "out", weighted = NULL)

# Delete isolates from the twomode network (optional)
Isolated <- which(degree(two_mode_network)==0)
two_mode_network_ni <- delete.vertices(two_mode_network, Isolated)
class(two_mode_network_ni)

# Transform twomode back to an incidence matrix
M2 <- as_incidence_matrix(two_mode_network_ni, names = TRUE)

# Make a heatmap of the incidence matrix (optional)
heatmap (M2, Colv = NA, scale = "row", cexCol = 1, xlab = "Concept")

#calculate jaccard distances for actors and concepts
actor_jaccard <- dist.binary(M2, method=1, upper=FALSE, diag = FALSE) # Method #1 is "Jaccard Index"
concept_jaccard <- dist.binary(t(M2), method=1, upper=FALSE, diag = FALSE) 

# create one-mode matrixes based on jaccard normalization, set diagonals zero
## Note that the Jaccard coefficient is calculated as type type d = sqrt(1 - s), so no similarity is reflected by 1, whereas 0 reflects a perfect match/ no distance at all
om_actor <- as.matrix(actor_jaccard)   
diag(om_actor)<-0
om_concept <- as.matrix(concept_jaccard)
diag(om_concept)<-0

#The Jaccard coefficients can be retained by applying the function (((x)^2)-1)*(-1)) to any value x
M3 <- apply(om_actor,1:2, function(x) (((x)^2)-1)*(-1))
M4 <- apply(om_concept,1:2, function(x) (((x)^2)-1)*(-1))

#Set diagonals of Jaccard normalized one-mode networks zero
diag(M3)<-0
diag(M4)<-0

#Print to csv
write.table (M3, file ="om_actor_jaccard.csv", dec = ".", sep = ";", col.names = NA)
write.table (M4, file = "om_concept_jaccard.csv", dec = ".", sep = ";", col.names = NA)
write.table (M2, file = "tm_unweighted.csv", dec = ".", sep = ";", col.names = NA)