Rense Corten

I am pleased to announce the publication of my latest paper in PLoS ONE, titled “Composition and Structure of a Large Online Social Network in the Netherlands.” The paper studies the network structure of Hyves, until recently with about 10 million members the most popular online social network in the Netherlands. For this study I used a unique complete snapshot of the network, courteously provided by Hyves.nl. Here’s the abstract:

Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in the Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for the Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

Get the paper here!

Harvard’s Ethan Fosse blogs about his top five unsolved sociological questions. Regardless of whether I agree that these particular ones are the “top five” (although I think all of them are interesting and relevant), I fully agree that making such lists is important. The lack of consensus about our main problems is one of the issues preventing progress in sociology, and open discussion about what we find important should bring us closer to such a consensus.

Having said that, I don’t really agree that Ehan’s number three ( Why do so many cultural and social phenomena … follow power-law distributions) should be in the top five, because it is not so clear that this is even true (e.g., see here). Also, I miss questions about social structure in the sense of social networks (obviously because I happen to study them ). For example, to what extend is the social structure of modern societies changing under the influences of mass immigration and technological progress?

• Cambridge University Press launches a new journal called Network Science. The editorial board looks impressive, with prominent names from various disciplines that deal with networks, including sociology, economics, physics, and computer science. Still, I wonder if they hope to compete with Social Networks? Maybe not if they want to focus on networks “in general” but it seems a bit silly to me to build a journal around a paradigm rather than a set of research questions.
• Fabio Rojas at Orgtheory launches an “open source”  paper project, trying to figure out how “holes” in a grid would influence segregation in Schelling’s model.
• This one is a bit old, but still – the New York Times has a review of Duncan Watts’ book “Everything is Obvious” by Nicolas Christakis. It is a nice review, although I don’t quite agree with Christakis that Watts wants to “debunk methodological individualism.” He uses some micro-macro models to illustrate the unpredictability of social phenomena, I don’t think that leads him to the conclusion that methodological individualism per se is flawed.

A recent issue of Footnotes, the newsletter of the American Sociological Association, includes an article by Scott Golder and Michael Macy on the use of data generated by social media for social science. They nicely highlight a number of good examples of such research, and echo Duncan Watts’ sentiment (in Everything is Obvious) that social media research may be as revolutionary for social science as the invention of the telescope was for astronomy. Maybe even more importantly, they emphasize that currently sociologists are not ready to face that challenge, and that unless they beef up their computational skills, computer scientists will run off with all the low-hanging fruit. Hear, hear!

I just uploaded a new working paper on SSRN, titled “Composition and Structure of a Large Online Social Network in the Netherlands.” The paper describes an exploratory analysis of a unique dataset provided by Hyves, the largest online social network in the Netherlands, with currently over 11 million members. Here’s the abstract:

Limitations in data collection haven long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. In this study, we use complete network data from a popular online social networking service in the Netherlands, Hyves, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for the Netherlands, we examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. First results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

Download the PDF from here.

For the tenth time, we organize our Workshop on Networks in Economics and Sociology at Utrecht University, as a co-production of the department of Sociology and the Utrecht School of Economics. To celebrate this anniversary, we extended the program to two days and managed to put together an exiting program of talks, including no less than three great keynote speakers. More information on the program and registration  is here. Note that registration for talks is closed; by now you can only register as a participant.

I just got the following message from Elsevier:

I would like to inform you that the Editors of Social Network have decided to change the current reviewing policy from ‘Double Blind’ ( Authors and reviewers details are not discosed to each other) to ‘Single Blind’ (author details are shared with Reviewers) to streamline the submissions.

That is quite revolutionary! I know of no other (semi-)top journal that doesn’t do double-blind peer review, considered by many the hallmark of objective quality assurance in science. It is all the more surprising that the editors (via Elsevier) present it as merely an efficiency measure. I bet there will  be a lot of discussion about this, and I am very curious about the substantive arguments that the editors may  have to justify this surprising move…

I’m in the process of moving back to the Netherlands, so I only have time to post a few links:

• Some Swiss researchers show through network analysis how the world of multinational corporations is dominated by a small but powerful core (via Science News)
• Orgtheory has a nice post on the problem of excessive journal review times, one of my favorite frustrations
• Couchsurfing.org (which also appeared in a recent post,) recently made the decision to transform itself i from a non-profit organization into a real corporation. Without doubt, this move will be very controversial among the more idealistic couchsurfers. It will be very interesting to see if and how this digital age grass-roots organization will survive.

My collaborator Bogdan State of Stanford University created a video of the evolution of a local “Couchsurfing” network, as part of our research project on the evolution of trust within the Couchsurfing movement. Couchsurfing is an international community through which travelers can find a place to spend the night (aka a “couch”).Among other things, we are interested in how the obvious trust problems involved with having a stranger spend the night in your house are solved, and how Couchsurfing  creates local communities, both online and offline. The video below focuses on Couchsurfers within San Francisco, and shows how the social network among them evolves.

From the description of the video on YouTube:

Blue ties represent friendships from outside the organization. Red ties represent friendships formed within the CouchSurfing organization. We have no information about grey ties. The width of tie is proportional with the indicated strength of the friendship: i.e., from “acquaintance” to “best friend.”
The movie was done in SoNiA, a highly-recommended free dynamic network visualization tool.

One of the cool things to observe in this video is that the read ties, which were formed through Couchsurfing, appear to form the core of the network and to be crucial in keeping the large community together. Also, you can observe the rather sudden emergence of a giant component.

My coauthor Michał Bojanowski just uploaded our working paper “Measuring Segregation in Social Networks” to SSRN. Here’s the abstract:

Network homophily is a pattern in which ties are more likely to exist between nodes similar to each other. It is frequently observed for various types of social relations. At the same time, segregation is often encountered in urban areas as a tendency of families to occupy neighborhoods inhabited by other families similar to them. In this paper we conceptualize both phenomena as in the language of networks of interlinked positions occupied by a population of actors characterized by some node-level attribute. We review existing indexes and approaches to measuring the extent of homophily/segregation in social networks. We pursue an axiomatic-like approach by first specifying a set of basic properties that a generic segregation measure might possess, and which, in our view, are relevant in substantial contexts. We check which measures satisfy which properties. Given the particular application and the need for some descriptive measure of segregation, the results presented in this paper can help in selecting an optimal measure for the task at hand. We conclude that the most crucial aspects for the choice of a particular segregation measure include (1) whether the network ties or actors’ attributes are assumed to be subject to change, and (2) how one should treat the presence of network isolates.

Get it here.