Skip to content

Tag: music visualization

Music Network Visualization

Note: probably of interest only to the intersection of the readers who are into niche music genres and those interested in network visualization. My music interests have always been rather, hmm…, eclectic. Somehow IDM, ambient, darkwave, triphop, acid jazz, bossa nova, qawali, Mali blues and other more or less obscure genres have managed to happily co-exist in my music collection. The sheer diversity always invited the question whether there is some structure to the collection, or each genre is an island of its own. Sounds like a job for network visualization! Now, there are plenty of music network viz applications on the web. But they don’t show my collection, and just seem unsatisfactory for various reasons. So I decided to craft my own visualization using R and igraph. As a first step I collected for all artists in my library the artists that the site classifies as similar. So I piggyback on for the network similarity measures. I also get info on the most-often used tag for the artist and the number of plays it has on the site. The rest is pretty straightforward as can be seen from the code. # Load the igraph and foreign packages (install if needed) require(igraph) require(foreign) lastfm<-read.csv(“”, header=T, encoding=”UTF-8″) #Load the dataset lastfm$include<-ifelse(lastfm$Similar %in% lastfm$Artist==T,1,0) #Index the links between artists in the library<, directed=F) #Import as a graph last.attr<-lastfm[-which(duplicated(lastfm$Artist)),c(5,3,4) ] #Create some attributes V([1:106]$listeners<-last.attr[,2] V([107:length(V(]$listeners<-NA V([1:106]$tag<-last.attr[,3] V([107:length(V(]$tag<-NA #Attach the attributes to the artist from the library (only) V($label.cex$tag<-ifelse(V($listeners>1200000, 1.4, (ifelse(V($listeners>500000, 1.2, (ifelse(V($listeners>100000, 1.1,…