Twitter is emerging as an important medium for determining influence in many fields. Social ranking sites like Klout and Traackr include Twitter as a heavily-weighted component of their ranking algorithms, for example. Twitter isn't representative of the members of any field, but in areas where the members primarily engage online, it can be a useful proxy. SocialFlow's Gilad Lotan has used an analysis of social networks on Twitter to rank influences in such fields, including the communities of python users and data scientists. Here's the network chart for data scientists:
Each circle represents a data scientists on Twitter (anyone with Data Science, Data Scientist, Machine Learning, Data Strategy or the like in their bio). The circle size is proportional to their influence during a week this past October. If you're active in the R or Data Science communities on Twitter, you'll recognize many of the names: Hilary Mason (@hmason) from bitly, Pete Skomoroch (@peteskomoroch) from LinkedIn; DJ Patil (@dpatil), author of Building Data Science Teams, and #rstats regulars Ryan Rosario (@datajunkie), John Myles White (@johnmyleswhite) and Mark Alen (@siah). (I'm down there in the bottom right corner: @revodavid.)
The colors represent connected clusters within the social graph, detected autonomously. With Hilary Mason's help, Gilad assigns meaning to these clusters:
Purple seems to be a mix of east coast and academics, while the dark blue is the west coast data drinking crew. Yellow looks like west coast social network folks while green have been doing it for a while. Although @BigDataBorat is identified within that segment… hmmm… The orange cluster is harder to nail down. Perhaps more academic, applied math and less tech-scene?
The analysis of the social network code was performed with Python, and the visualization was created with Gephi. You can find the details of how it was done, including slides and Python code, at the link below.
Gilad Lotan: Mapping Twitter’s Python and Data Science Communities (via Quora)