Use Visone to make your first network map

I often get asked about network mapping software. I switch back and forth between several programs depending on what I want to do. For most, I usually recommend NetDraw. However, for those wishing to make very simple maps, another excellent choice is Visone.

You can easily run Visone with the "webstart" option available here. Once you have it running, the picture below hints at how easy it is to (1) give yourself a starting set of nodes by creating a "random" graph with no edges, and (2) use "edit mode" to make your nodes look as you wish and add edges between them. Then you can use "analysis mode" to drag nodes around and try different automatic layouts, etc.I use Visone to make all the illustrations in my Introduction to Network Mathematics, but I don't use it for consulting because it is licensed only for non-commercial use.

This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.

Information advantage

I attended a talk recently about computational sociology and data mining. The speaker began with a claim that technology is never policy-agnostic but almost always advocates for some policy or other.

Half-way through the talk, someone referred to the impressive array of technology employed by the speaker's research and asked what policy that technology was advocating. The speaker deftly avoided the question by raising policy questions without answering any of them. He was policy-agnostic, you might say.

In such situations (and many others), it is a safe bet that the policy being advocated by the technologist is "I deserve your respect, money, and/or votes."

The best case I have seen for this argument was put forth by Robert Thomas in his book, What Machines Can't Do, which I originally mentioned here with respect to user-driven innovation.

I agree with Thomas, and certainly hope that my blog wins me your respect, money, and/or votes. Let the world know how much you admire my wisdom and power:

This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.

When nothing is done, nothing is left undone

This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.

Bridging the gap between structuralist and individualist approaches to social networks

My book report on Social Networks and Organizations, by Kilduff and Tsai. Part Four of a Series.

Chapter Four: Bridging the gap between structuralist and individualist approaches to social networks. Kilduff and Tsai shine an unflinching and highly amusing light on this seemingly religious rivalry--another good case study for Bion and Shirky.

This chapter, in my opinion, is the single best chapter of the book and all by itself justifies the book's $45 purchase price.

Kilduff and Tsai show us the soap opera of real science. In this episode, individualists have thrown down the gauntlet and decried "the tendency in network analysis towards 'overelaboration of technique and data and an accumulation of trivial results.' (Boissevain)"

In response, "Network researchers tend to be united in their adherence to ... the anti-categorical imperative. This imperative, 'rejects all attempts to explain human behavior ... in terms of categorical attributes of actors.' (Emirbayer)"

Kilduff and Tsai go on, "The typical start to any social network article often involves a ritualistic swipe at those who have previously focused on the attributes of individuals."

After showing us the soap opera, the authors conclude: "There is a pressing need for non-dogmatic research that explores issues concerning how individual differences in cognition and personality relate to the origins and formations of social networks."

Recommend further reading:

Kilduff, M. and Krackhardt, D. 1994. Bringing the individual back in: A structural analysis of the internal market for reputation in organizations. Academy of Management Journal, 37:87-108.

Krackhardt, D. and Kilduff, M. 1999. Whether close or far: Social distance effects on perceived balance in friendship networks. Journal of Personality and Social Psychology, 76:770-82.

Kumbasar, E.A., Romney, K. and Batchelder, W.H. 1994. Systematic biases in social perception. American Journal of Sociology, 100:477-505.

Mayhew, B.H. 1980. Structuralism versus individualism. Part 1: Shadow boxing in the dark. Social Forces, 59:335-75.

Mehra, A., Kilduff, M. and Brass, D.J. 2001. The social networks of high and low self-monitors: Implications for workplace performance. Administrative Science Quarterly, 35:121-46.

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I hate physicists; Barry Wellman is God

I attended a talk recently that reminded me of the not-so-hidden rivalry between sociologists and physicists who study networks. Conveniently, my notepad that day was the backside of my printout of "A Group Is Its Own Worst Enemy" by Clay Shirky. In this brilliant essay, Shirky explains how group dynamics take hold quickly and then tend to lead participants into three deep behavioral ruts (quoted from Bion):

1. Find sex partners (ladies, see my email link in the right sidebar)
   
2. Identify and vilify external enemies (physicists)
   
3. Venerate religious idols (Barry Wellman)
   

Physicists, sociologists, and network gurus of all stripes engage in these behaviors as much as anyone.

For all its brilliance, Shirky's essay suffers major flaws. He argues that "learning from experience is the worst possible way to learn something." I refer readers to group behavior pattern #1 for my first counter-example to this bizarre claim. Shirky also says, "Prior to the Internet, the last technology that had any real effect on the way people sat down and talked together was the table." By my estimation, the table pre-dates literacy, and so Shirky is ranking broadband access as more significant to talking than both reading and writing. Does that sound right to you?

I hope that my readers will check out Shirky's highly stimulating essay and come back to Connectedness for when I argue that the very title of his essay, "A Group Is Its Own Worst Enemy," is as problematic as the above two quotes.

This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.

Mapping all of science

Back in 1939, J. D. Bernal prefaced his 500-page treatise "The Social Function of Science" with these words:

"Science has ceased to be the occupation of ... ingenious minds supported by wealthy patrons and has become an industry supported by large industrial monopolies and the state. Imperceptibly this has altered the character of science from an individual to a collective basis, and has enhanced the importance of apparatus and administration."

Add the above passage to my list of retorts to proclaimers of the "dawn of emergent collaboration." Then flip ahead with me 280 pages to the one picture in the book, ambitiously titled "The Organization of Science":
Click on the picture to see the full map.

The moment I saw this map it reminded me of Katy Borner's work at Indiana University, which is part of the traveling exhibit, "Places & Spaces: Mapping Science." This exhibit includes a "Map of Scientific Paradigms" by Boyack and Klavens:What important information about "Science" is communicated by these outstanding maps? There is no simple answer to this question. For me, the most important information a map can convey is a sense of which places are close together and which are far apart. Others design their network visualization tools based on different priorities (e.g., NetViz Nirvana by Shneiderman and Aris).

Geographic cartography is already complicated enough to be a science in its own right. Network cartography is at least as complicated, thanks in large part to its indifference to the triangle inequality--the most fundamental property that mathematicians usually require of anything that purports to measure "closeness" and "farness." (See "Identity and Search" in Science for more on the subtleties of social network distance.)

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A lesson in network visualization from John Maeda

Yesterday's NY Times featured a striking network map as the cover art for its annual real estate magazine:
Even better, here is a slide show with MIT Media Lab's John Maeda telling how he came up with the design, which he originally conceived as "Google mappish Mondrian. Sort of Pollack meets Mondrian."

This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.