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

library ("igraph")
library ("lsa") #for cosine normalization

# Loading two-mode matrix with edge weights into matrix object 
M = as.matrix(read.csv(file.choose(), sep = ";", header = T, row.names = 1, check.names=FALSE))
class(M)

#Transform matrix object into a weighted, directed, twomode igraph object (Cosine normalization works best with weighted matrices)
two_mode_network <- graph.incidence(M, directed = TRUE, mode = "out", weighted = TRUE)

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

# 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 cosine distances for actors and concepts
actors_cosine <- cosine (t(M2))
concept_cosine <- cosine (M2)

# create one-mode matrixes based on cosine normalization, set diagonals zero
om_actors <- as.matrix(actors_cosine)   
diag(om_actors)<-0
om_concept <- as.matrix(concept_cosine)
diag(om_concept)<-0

#Print to csv
write.table (om_actors, file ="om_actors_cosine.csv", dec = ".", sep = ";", row.names = TRUE, col.names = NA, quote = FALSE)
write.table (om_concept, file = "om_concepts_cosine.csv", dec = ".", sep = ";", row.names = TRUE, col.names = NA, quote = FALSE)