LinkedIn Maps: art or science?

LinkedIn is a great resource for exploring professional profiles. However, when your personal social network starts to grow into the hundreds of contacts, it becomes very hard to – quite literally – still see the bigger picture. One feature that can help you visualize your network is LinkedIn Maps. It both shows the links between your connections and color-codes major clusters that are rough approximations of the various professional and personal worlds you move around in. You can zoom in and out, and select individual contacts to see which persons you know they are also connected to.

Of course, it makes for pretty art. However, the maps can also be useful. First, they give you a quick sense of the roles you play in your social world, through the colored sub-networks. Another use is to find out which people who you think don’t know one another, in fact are acquainted.

My LinkedIn Map – Overview

To give you an idea of what LinkedIn Maps is about in practice, here are some of my own maps. First, the total overview, showing the “regions of my personal world map” (click on the figures to see the details). For instance, one big region is formed by my local Tilburg connections, other regions by my Tilburg University research contacts, my international Community Informatics research connections, etc.

Zooming in on my personal network

The closer the regions are to my own node, and the more densely connected they are, the more they represent my “daily social circles”. When zooming in, the names of individual connections become visible.  The bigger the dots depicting my contacts, the more they are connected to my own contacts, and the more likely we have something in common, if only by knowing the same people.

Zooming in on a close “general connector” who is well connected to many of the people I know across many of my social circles

Finally, by selecting particular contacts, you can quickly explore which of your contacts in the various regions you share. This can be very valuable information, in, for example, setting up joint projects, selecting network coordinators or community managers who need to act as “spiders in your webs”, and so on. In this figure, I have selected one of my close contacts, and immediately see he is quite evenly connected to all of my “daily networks”. If I were to set up a project involving those networks, he would be a good candidate to ask for assistance.

Zooming in on a “specialized connector” who is well connected to many of the people I know in one particular circle

On the other hand, the contact I selected in this example, is very much connected to many of the people in my subnetwork that I have dubbed my “Tilburg University research network”. So, if I were to set up a joint research project with my former colleagues, he would be one of the persons to talk to first!

Tag clouds on the move

Yesterday, I discussed Wordle. Today, I came across a related tool, TagCrowd:

TagCrowd is a web application for visualizing word frequencies in any user-supplied text by creating what is popularly known as a tag cloud or text cloud.

TagCrowd is taking tag clouds far beyond their original function:

  • as topic summaries for speeches and written works
  • for visual analysis of survey data
  • as brand clouds that let companies see how they are perceived by the world
  • for data mining a text corpus
  • for helping writers and students reflect on their work
  • as name tags for conferences, cocktail parties or wherever new collaborations start
  • as resumes in a single glance
  • as visual poetry

Interestingly, both tools seem to indicate the growing realization that tag clouds have many more uses than their original, narrow application for indicating blog topic frequencies. A good example of the how tools often get used for very different purposes than what they were originally designed for!

Another application of “serious tagging” is not to use one tag cloud for various purposes, but to compare tag clouds.  Lilia Efimova gives a nice illustration of how she compared the tag clouds of her blog posts and a dissertation chapter on the same topic. Another comparison is to see how different tag cloud tools process the same text. Here’s the TagCrowd interpretation of the CommunitySense home page:

Quite a diffferent look and feel from the one provided by Wordle, right? It would be interesting to come up with visualization criteria which provide the best type of tag cloud for the particular purpose for which they are used.

Word (art) clouds

A friend pointed out Wordle to me, which “is a toy for generating ‘word clouds’ from text that you provide. The clouds give greater prominence to words that appear more frequently in the source text. You can tweak your clouds with different fonts, layouts, and color schemes.”

I tried it out with the text on the CommunitySense home page:

Apart from truly being a piece of art and aesthetically pleasing, such “tag clouds ++” should have real business applications. It would be interesting to see how, say, a 100 page report would look like and whether its visualization could help in quickly grasping some of its essential meaning.

Schomer Simpson’s Talk at the 1st Second Life (Inworld) Conference


Below the gallery that contains the pictures taken by Al Mohr (Second Life) / Aldo de Moor (Real Life) of Schomer Simpson ‘s (Second Life) / Peter Twining’s (Real Life) presentation at the Second Life Best Practices in Education International Conference 2007. The topic was “Using Teen Second Life to Explore Visions of Schome (Not School-Not Home-Schome, the Education System for the Information Age)”. As you can see from the pictures, Schomer/Peter’s talk was very well attended. The issues raised were most interesting and he got lots of questions. The Era of Immersive Online Conferences has begun…

Picture gallery

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