useR! poster: ranking influential communities

June 29, 2017

(This article was first published on R on Tea & Stats, and kindly contributed to R-bloggers)

Next week I will be presenting a poster at the useR!2017 conference in Brussels.

My topic is Ranking influential communities in networks.
Using a large dataset of citations from the Web of Science, we grouped academic journals into communities based on their citation behaviour.
These communities closely correspond to recognisable research fields, so I was able to label them.

We then modelled the flow of influence within and between these communities.
A journal having a high influence score means it tends to receive more citations than it gives out when interacting with other influential journals.

Statistics comes second in the inter-field ranking (after economics).
We all know how cool statistics is, so the model must be right.

useR!2017 poster

Here is a preview.
You can download the full PDF on the conference web site.

The poster is mostly reproducible: the full R source code for the analysis is available on GitHub, as is the Scribus file that generated the poster itself.
I say ‘mostly’ because the code is open but unfortunately the raw data is not.

In a maximally reproducible workflow, we could even consider designing the entire poster with Markdown.
An idea for a future R package, perhaps?

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