Co-author networks in Criminology

In my bin of things I will never finish at this point, I started a manuscript looking at co-author networks in criminology using web of science data. I recruited several folks over the years (grad students at the time Jen Laprade and Richard Hernendez, and Marie Oullet), but I was never able to put in the last bit of time to finish it off. Exploratory work is hard, as there is no end goal to work towards. So I was never able to get it to a point I was happy with.

The shamble of the current paper is here, which will contain more details than this post. But basically I downloaded all of the Web of Science data that had the CJ/Crim label attached up to 2016, then turned that into a co-author network.

So the way it works is if I co-authored an article with Rob Worden & Sarah McLean, and Rob Worden & Sarah McLean co-authored a paper with Chris Harris, me and Chris are not directly connected, but are just 1 degree apart. After doing this, I wanted to see if we clustered into different groups. The answer to that is yes, I can get the computer to spit out clusters (colored below), but we are still definately small world (everyone is connected to everyone one with only a few hops).

I had a really hard go at it to get the networks to layout nicely (a typical problem with big, interconnected networks). I’ve posted an interactive version here. You can zoom in, look at the clusters, and look yourself up.

Here is a GIF showing surfing the network. I look up Beth Huebner (I would say Beth is part of the Michigan State/CJ folks Cluster), see she is attached to Scott Decker (who is in another blue cluster that has a pretty big array of folks, it has many Arizona but also Alex Piquero, Dan Nagin, and Shawn Bushway), then go onto Scott Wolfe etc.

I figured the clusters would be by topical area (which is true to a certain extent), but they were also by University clusters. Here was my attempt to give some meaning to the clusters, by pulling out the top 3 authors/journals. There are some 40 clusters in the excel file in the paper folder shared earlier. (There are more clusters than that even, but they are the 40 biggest in terms of authors/articles.)

So that gives some face validity to the clusters, but like I said it is small world, so maybe that isn’t worth noting at all anyway. One of the things I noticed was that the clusters had a big seperation between USA folks and international folks.

So if someone wants to take this over let me know. I didn’t share a link to the data directly (I imagine that violates the Web of Science terms of service.) But will share offline plus my code if someone wants it. (It is already 3+ years old data, I don’t even want to think about updating the work. Jen and Richard did a bunch of grunge work to clean the names for me to make the network.)

Coauthorship over time

One thing I noted was the change in co-authorship over time. It is a perpetual question about how to evaluate folks by solo-authorship. I can’t answer that question, but we can observe how it is changing over time. Here are graphs of proportion solo over time, as well as the mean number of authors over time (with error intervals, much more data in recent years than past).

This holds true the same for our top journals (the WOS data is quite a hodge podge, including forensic pysch, some trade magazines, etc.).

Citations Over Time

Another example bit of data analysis I did with this dataset is you can look at citations over time as well. Here is the mean of citations in well known crim/cj journals over time.

And here is a scatterplot of the individual papers. I’ve posted an interactive version of this as well.

So more stuff than I can handle zipping around this data. (I tried to make some sense of keywords for articles at one point, but that would take some more serious semantic reduction of like words.)

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