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Update 2015-11-09: This is migration from an old post.
I took the SNA course by Lada Adamic in coursera. It’s a super interesting course. In fact, I was using the networks only how a visualization tool, and that is what it make me little bit embarrassing because there are more, a lot of more. You can detect communities, know more centric nodes and a lot of other information. So, there are a lot of reasons to look the course
By other hand I like the d3 javascript library. Recently I was learning javascript, so I decided make a very little app to keep learning this library and show differents measures of centrality for each node in a set of 4 toy networks and see these measures by size, color or a label
Now, the R code to make the data:
rm(list = ls()) ##### Load Packages #### library("sna") # for build a block diagonal matrix library("Matrix") # (https://stat.ethz.ch/pipermail/r-help/2007-June/133875.html) library("reldist") library("plyr") library("rjson") ##### Functions #### degree_sna <- function(net, norm = TRUE, ...){ degree(net, ...)/2/(if (norm) ncol(net) - 1 else 1) } betweenness_sna <- function(net, norm = FALSE, ...){ n <- ncol(net) betweenness(net, ...)/2/(if (norm) (n - 1)*(n - 2)/2 else 1) } ##### Networks #### net.butterfly <- matrix(c(0,1,1,0,0,0,0, 1,0,1,0,0,0,0, 1,1,0,1,0,0,0, 0,0,1,0,1,0,0, 0,0,0,1,0,1,1, 0,0,0,0,1,0,1, 0,0,0,0,1,1,0), byrow = TRUE, nrow = 7) net.star <- matrix(c(0,1,1,1,1,1, 1,0,0,0,0,0, 1,0,0,0,0,0, 1,0,0,0,0,0, 1,0,0,0,0,0, 1,0,0,0,0,0), byrow = TRUE, nrow = 6) net.line <- matrix(c(0,1,0,0,0, 1,0,1,0,0, 0,1,0,1,0, 0,0,1,0,1, 0,0,0,1,0), byrow = TRUE, nrow = 5) net.circular <- matrix(c(0,1,0,0,1, 1,0,1,0,0, 0,1,0,1,0, 0,0,1,0,1, 1,0,0,1,0), byrow = TRUE, nrow = 5) nets <- list(net.butterfly, net.star, net.line, net.circular) net.all <- as.matrix(bdiag(net.butterfly, net.star, net.line, net.circular)) ##### Plots #### gplot(net.butterfly, displaylabels = TRUE, usearrows = FALSE)
gplot(net.star, displaylabels = TRUE, usearrows = FALSE)
gplot(net.line, displaylabels = TRUE, usearrows = FALSE)
gplot(net.circular, displaylabels = TRUE, usearrows = FALSE)
gplot(net.all, usearrows = FALSE, label = unlist(llply(nets, degree_sna, norm = FALSE)))
#### Indicators #### # Degrees for each node of each network llply(nets, degree_sna)
## [[1]] ## [1] 0.333 0.333 0.500 0.333 0.500 0.333 0.333 ## ## [[2]] ## [1] 1.0 0.2 0.2 0.2 0.2 0.2 ## ## [[3]] ## [1] 0.25 0.50 0.50 0.50 0.25 ## ## [[4]] ## [1] 0.5 0.5 0.5 0.5 0.5
llply(nets, degree_sna, norm = FALSE)
## [[1]] ## [1] 2 2 3 2 3 2 2 ## ## [[2]] ## [1] 5 1 1 1 1 1 ## ## [[3]] ## [1] 1 2 2 2 1 ## ## [[4]] ## [1] 2 2 2 2 2
# Differences beetween degree for nodes in each network laply(nets, function(net){ gini(degree_sna(net)) })
## [1] 0.0893 0.3333 0.1500 0.0000
laply(nets, function(net){ sd(degree_sna(net)) })
## [1] 0.0813 0.3266 0.1369 0.0000
# Centralization coefficient $C_D$ laply(nets, centralization, degree)
## [1] 0.167 1.000 0.167 0.000
# Betweenness llply(nets, betweenness_sna)
## [[1]] ## [1] 0 0 8 9 8 0 0 ## ## [[2]] ## [1] 10 0 0 0 0 0 ## ## [[3]] ## [1] 0 3 4 3 0 ## ## [[4]] ## [1] 1 1 1 1 1
llply(nets, betweenness_sna, norm = TRUE)
## [[1]] ## [1] 0.000 0.000 0.533 0.600 0.533 0.000 0.000 ## ## [[2]] ## [1] 1 0 0 0 0 0 ## ## [[3]] ## [1] 0.000 0.500 0.667 0.500 0.000 ## ## [[4]] ## [1] 0.167 0.167 0.167 0.167 0.167
# Closeness llply(nets, closeness)
## [[1]] ## [1] 0.400 0.400 0.545 0.600 0.545 0.400 0.400 ## ## [[2]] ## [1] 1.000 0.556 0.556 0.556 0.556 0.556 ## ## [[3]] ## [1] 0.400 0.571 0.667 0.571 0.400 ## ## [[4]] ## [1] 0.667 0.667 0.667 0.667 0.667
# Eigenvector Centrality llply(nets, evcent)
## [[1]] ## [1] 0.335 0.335 0.450 0.384 0.450 0.335 0.335 ## ## [[2]] ## [1] 0.408 0.408 0.408 0.408 0.408 0.408 ## ## [[3]] ## [1] 0.309 0.463 0.617 0.463 0.309 ## ## [[4]] ## [1] 0.447 0.447 0.447 0.447 0.447
#### Consolidate Data #### names <- paste(rep(1:length(nets), laply(nets, ncol)), unlist(llply(nets, function(x) 1:ncol(x))), sep = "_") colnames(net.all) <- names rownames(net.all) <- names links <- ldply(names, function(name){ # name <- sample(names, size = 1) # name <- names[1] data.frame(source = which(names == name) - 1, target = which(net.all[name,] == 1) - 1) }) nodes <- data.frame(name = names) nodes$degree_norm <- unlist(llply(nets, degree_sna)) nodes$degree <- unlist(llply(nets, degree_sna, norm = FALSE)) nodes$betweenness <- unlist(llply(nets, betweenness_sna)) nodes$betweenness_norm <- unlist(llply(nets, betweenness_sna, norm = TRUE)) nodes$closeness <- unlist(llply(nets, closeness)) nodes$eigen_vector_centrality <- unlist(llply(nets, evcent)) #### Exporting Data #### nodes_json <- adply(nodes, 1, toJSON )$V1 nodes_json <- paste(" "nodes" : [", paste("n", nodes_json, collapse = ", "), "n]") links_json <- adply(links, 1, toJSON)$V1 links_json <- paste(" "links" : [", paste("n", links_json, collapse = ", "), "n]") data_json <- paste("{n", nodes_json, "n,n", links_json, "}") # write(data_json, "data.json")
You can fork the repo from here.
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