Social networks and XY

That’s the really interesting stuff, combining the analysis of social networks with the analysis of something else (XY): What kinds of networks are linked to greater innovations in companies (you need to collect indicators of innovativeness in addition to the network analysis), higher welfare in African villages (add welfare indicators) or being more effective terrorists (count successful acts of terrorism). That’s all about the question of how networks lead to something. And it get’s really mind boggling if you are able to link these results to specific structural issues of networks, such as centralization, flexibility over time, structural holes etc.

On the other hand you might also want to know what leads to people achieving certain positions within networks, what makes someone a hub in a hub-and-spoke network, why are some people so much better connected than others, act as boundary spanners or bottle-necks? Is it all about personality, income, geographic location, organizational structure, time spent in a specific field, cultural background or something completely different?

When people are exited about the new kind of data and understanding you can gather with social network analysis, they (and I am part of “they” here) often don’t realize that your understanding can move to the next level, if you combine network with non-network data.

Is there anyone out there who has combined social network and geographic data? I have this visual of a presentation in my head: First you show a social network map with the actors with highest centrality in the middle and the lesser connected nodes at the fringes (i.e. standard visualization of networks), on the next slide you see how these actors are distributed in space (e.g. where their farms are or where their offices are located) and then you put the social network on top of the geographic and the actors slowly move to their place in space while maintaining their network links. Not only would that look cool, but you would also be able to give your audience a very direct feel for whether or not the social networks are linked to the geographic position of the actors: Do they have stronger links with their immediate neighbors? Does the guy who lives in the middle of the village (or whose office is by the water cooler) really have the most dense network?

At the moment I neither have the data to play with nor would I know how to animate it, but maybe there is someone out there, who has already done that? Or, maybe you have the data and want to discuss how you could best visualize it? Or you are planning a research project and think: Wow, this is how I’m going to do it! But how am I going to do it? Drop me a line.

4 Responses

  1. Hi! We combined network analysis (co-pubs and co-patents) with geographic maps and compared two innovation regions: Saskatoon, Canada and Skanian region in Sweden. The combination (at the time) was achieved with a little bit of duct tape and a lot of chewing gum, I am afraid. GIS helped. But ORA (SNA program offered through CASOS at Carnegie Mellon U) has now integrated (as of June 2008) GIS into its programming. Although not completely seamless as yet, it is an indicator of abilities to come and new study/research options. If you would like to check out our Saskatoon/Skanian study, I would be happy to send you the article (published in 2006 in Industry and Innovation (journal)). Let me know!!!!


    • Dear Cami,
      That sounds very interesting. Yes I would be interested in the document. Would it be possible that I share it on this blog? Is the paper open access?

  2. Hi Cami,
    Yes I have worked on geographic network data with the National Cancer Institute. Glimpse of it here:

    Network layout in general is ripe for new insights! I like Ulrik Brandes ( work in this area. Claire Reinelt and I speak to network layout and leadership networks as an important open topic here:, see “SNA and leadership networks”


  3. Sorry I meant “Hi Eva” not “Hi Cami” 🙂 oops

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