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Category: Network analysis

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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The hidden structure of (academic) organizations

Published by demetriodor on October 23, 2012

All organizations have a ‘deep’ hidden structure based on the social interactions among its members which might or might not coincide with the official formal one. University departments are no exception – if anything, the informal alliances, affinities, and allegiances within academic departments are only too visible and salient. Network analysis provides one way of visualizing and exploring the ‘deep’ organizational structure. In order to learn how to visualize small networks with R, I collected data on the social interactions within my own department and plugged the dataset in R (igraph package) to get the plot below. The figure shows the social network of my institute based on the co-supervision of student dissertations (each Master thesis has a supervisor who selects a so-called ‘second’ reader who reviews the draft and the two supervisors examine the student during the defence). So each link between nodes (people) is based on one joint supervision of a student. The total number of links (edges) is 264 which covers (approximately) all dissertations defended over the last year. In this version of the graph, the people are represented only by numbers but in the full version the actual names of people are plotted, the links are directional, and additional info (like the grade of the thesis) can be incorporated. Altogether, the organization appears surprisingly well-integrated. Most ‘outsiders’ and most weakly-connected ‘islands’ are either occasional external readers, or new colleagues being ‘socialized’ into the organization. Obviously, some people are more ‘central’ in the sense of connecting to a more diverse set of people, while others serve as boundary-spanners reaching…

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Facebook does randomized experiments to study social interactions

Published by demetriodor on June 15, 2012

Facebook has a Data Science Team. And here is what they do: Eytan Bakshy […] wanted to learn whether our actions on Facebook are mainly influenced by those of our close friends, who are likely to have similar tastes. […] So he messed with how Facebook operated for a quarter of a billion users. Over a seven-week period, the 76 million links that those users shared with each other were logged. Then, on 219 million randomly chosen occasions, Facebook prevented someone from seeing a link shared by a friend. Hiding links this way created a control group so that Bakshy could assess how often people end up promoting the same links because they have similar information sources and interests  [link to source at Technology Review]. It must be great (and a great challenge) to have access to all the data Facebook and use it to answer questions that are relevant not only for the immediate business objectives of the company. In the words of the Data Science Team leader: “The biggest challenges Facebook has to solve are the same challenges that social science has.” Those challenges include understanding why some ideas or fashions spread from a few individuals to become universal and others don’t, or to what extent a person’s future actions are a product of past communication with friends. Cool! These statements might make for a good discussion about the ethics of doing social science research inside and outside academica as well.

New tool for discourse network analysis

Published by demetriodor on April 11, 2012

EJPR has just published an article introducing a new tool for ‘discourse network analysis’. Using the tool, you can measure and visualize political discourses and the networks of actors affiliated to each discourse. One can study the actor congruence networks (based on the number of statements actors share), concept congruence networks (based on whether statements are used by an actor in the same way) and trace the evolution of both over time. Here is a graph taken from the paper which illustrates the actor congruence networks for the issue of software patents in the EU (click to enlarge): The discourse networks analysis tool is free and available from the website of Philip Leifeld, one of the co-authors of the article. I can’t wait to get my hands on the program and try it out for myself. The tool promises to be an interesting alternative to evolutionary factor analysis – another new method for studying policy frames and discourses that I recently discussed – with the added benefit of being able to present actors and frames in an integrated analysis.   Here is the abstract of the EJPR article (there are more resources at this website): In 2005, the European Parliament rejected the directive ‘on the patentability of computer-implemented inventions’, which had been drafted and supported by the European Commission, the Council and well-organised industrial interests, with an overwhelming majority. In this unusual case, a coalition of opponents of software patents prevailed over a strong industry-led coalition. In this article, an explanation is developed based on political…

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Predicting the votes of judges

Published by demetriodor on November 29, 2011

Here is a (short) and interesting paper that uses an innovative approach to predict the votes of the US Supreme Court: Successful attempts to predict judges’ votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice’s vote based on the other justices’ votes in the same case. For our predictions, we use models and methods that have been developed to uncover hidden associations between actors in complex social networks. We show that these methods are more accurate at predicting justice’s votes than forecasts made by legal experts and by algorithms that take into consideration the content of the cases. We argue that, within our framework, high predictability is a quantitative proxy for stable justice (and case) blocks, which probably reflect stable a priori attitudes toward the law. We find that U.S. Supreme Court justice votes are more predictable than one would expect from an ideal court composed of perfectly independent justices. Deviations from ideal behavior are most apparent in divided 5–4 decisions, where justice blocks seem to be most stable. Moreover, we find evidence that justice predictability decreased during the 50-year period spanning from the Warren Court to the Rehnquist Court, and that aggregate court predictability has been significantly lower during Democratic presidencies. More broadly, our results show that it is possible to use methods developed for the analysis of complex social networks to quantitatively investigate…

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