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Tag: igraph

Music Network Visualization

Published by demetriodor on December 11, 2012

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 last.fm library the artists that the site classifies as similar. So I piggyback on last.fm 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(“http://www.dimiter.eu/Data_files/lastfm_network_ad.csv”, header=T, encoding=”UTF-8″) #Load the dataset lastfm$include<-ifelse(lastfm$Similar %in% lastfm$Artist==T,1,0) #Index the links between artists in the library lastfm.network<-graph.data.frame(lastfm, directed=F) #Import as a graph last.attr<-lastfm[-which(duplicated(lastfm$Artist)),c(5,3,4) ] #Create some attributes V(lastfm.network)[1:106]$listeners<-last.attr[,2] V(lastfm.network)[107:length(V(lastfm.network))]$listeners<-NA V(lastfm.network)[1:106]$tag<-last.attr[,3] V(lastfm.network)[107:length(V(lastfm.network))]$tag<-NA #Attach the attributes to the artist from the library (only) V(lastfm.network)$label.cex$tag<-ifelse(V(lastfm.network)$listeners>1200000, 1.4, (ifelse(V(lastfm.network)$listeners>500000, 1.2, (ifelse(V(lastfm.network)$listeners>100000, 1.1,…

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Network visualization in R with the igraph package

Published by demetriodor on November 5, 2012

In this post I showed a visualization of the organizational network of my department. Since several people asked for details how the plot has been produced, I will provide the code and some extensions below. The plot has been done entirely in R (2.14.01) with the help of the igraph package. It is a great package but I found the documentation somewhat difficult to use, so hopefully this post can be a helpful introduction to network visualization with R. Here we go: # Load the igraph package (install if needed) require(igraph) # Data format. The data is in ‘edges’ format meaning that each row records a relationship (edge) between two people (vertices). # Additional attributes can be included. Here is an example: # Supervisor Examiner Grade Spec(ialization) # AA BD 6 X # BD CA 8 Y # AA DE 7 Y # … … … … # In this anonymized example, we have data on co-supervision with additional information about grades and specialization. # It is also possible to have the data in a matrix form (see the igraph documentation for details) # Load the data. The data needs to be loaded as a table first: bsk<-read.table(“http://www.dimiter.eu/Data_files/edgesdata3.txt”, sep=’t’, dec=’,’, header=T)#specify the path, separator(tab, comma, …), decimal point symbol, etc. # Transform the table into the required graph format: bsk.network<-graph.data.frame(bsk, directed=F) #the ‘directed’ attribute specifies whether the edges are directed # or equivelent irrespective of the position (1st vs 2nd column). For directed graphs use ‘directed=T’ # Inspect the data:…

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