Delete all your links, except to me

As part of the course re-design I mentioned last time, I have been digesting this excellent summary of Google PageRank that my students found for me a few weeks ago. I have boiled it down to this. Another way I teach my students PageRank is by entering all their semester web-programming projects into a PageRank contest, where the PageRank is determined by student recommendations entered into our class wiki.

The contest has one more week to go. You can see the front-runners vying for votes (or not) in the wiki excerpt below. They are listed in order of PageRank:

Site Link / Description	Incoming Links
http://people.bu.edu/gatewood/index.html Quit editing this wiki, get out and do something! My site is a December calendar of events, from concerts to festivals, that would interest a college age student in Boston.	choy alliew rgm ajhlee jihong88 cohens11 xzhang08 dbrien lbdunn
http://people.bu.edu/afacini/gaijin/ A resource site for learning how to read and write basic Japanese. Learn to write both hiragana and katakana, the basic alphabet scripts of Japanese.	jasonhu gaep13 wcmundel icc arthim edflem yasmin89 kevshea slee0903 dan013 abg cnm lbdunn
http://www.tormor.org The homepage of me and a friend of mine and our upcoming vegetable oil fueled cross-continental adventure.	jasonhu alliew tjpirog billba edflem yuliyab cohens11 koates andrak lbdunn
http://people.bu.edu/xzhang08/psych/index.html Enjoy looking at optical illusions? This website has lots of optical illusions and explains how each works!	gaep13 gillianb arthim yasmin89 timwalsh ajhlee mgro27 slee0903 dbrien jfsr lbdunn
http://people.bu.edu/anishk/Kattukaran-Webproject/Index.html (Firefox recommended) A website for a startup company that i am working on. The new venture will deal with educational services targetted at students from developing countries and aims to improve the standards of education in many of these countries.	aecook gaep13 icc haysher edflem dechat eeun zucchig bross55 lbdunn eskizzle
http://people.bu.edu/ajhlee/cs103/index.html This page is about how hip hop is dead	jihong88 cohens11 gatewood eeun jfsr edflem lbdunn

My favorite feature of PageRank is this strategy suggested by the PageRank article: "Outbound links are a drain on a site's total PageRank... But there are 'abnormal' ways of linking to other sites that don't result in leaks. PageRank is leaked when Google recognizes a link to another site. The answer is to use links that Google doesn't recognize or count."

This is true enough. To dramatize the point, I have provided my class with an Excel spreadsheet that takes the above wiki table as a copy-and-paste and instantaneously provides the resulting PageRank scores and relative class rankings. Each student can edit the resulting spreadsheet and see exactly how much his score goes up when he deletes all his outbound links and stops recommending other students' websites.

Hopefully, by the time I do the final PageRank calculation, all my students will have taken this lesson to heart and deleted all their links. You should too. Boost your visibility on the Internet by deleting all outbound links from all your blogs and other sites--except links to me. Thanks!

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.

Experiencing technical difficulty--please stand by

Major course redesign happening in front of a live student audience has Connectedness staff preoccupied. Regular blogging will resume shortly.

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.

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:

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When nothing is done, nothing is left undone

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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.

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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."

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Social networks and organizations: Critique of theoretical foundations

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

Chapter Three: Is There Social Network Theory? Kilduff and Tsai acknowledge this obtuse yet persistent question and explore the theoretical foundations of SNA.

Kilduff and Tsai lose me at this point in their book by being a bit too earnest in their treatment of this question. My objection to their earnestness has little to do with SNA and more to do with asking of anything, "Is this a method or a theory?" Using a favorite SNA metaphor to illustrate my point, imagine a serious conversation debating, "Is x-ray imaging a method or a theory?" This is a pragmatic question for academics deciding whether to grant PhDs and other awards to those working on x-rays. For the rest of us, who cares? I just want to know when x-rays are helpful and when they are not.

My personal favorite example of this sort of question is counting: "Is counting a method or a theory?" Most of us experience counting as a useful but humble technique, or method. But any fan of Georg Cantor can tell you that counting is also an extremely subtle realm of profound theory. Less mathematical readers may better identify with this example: "Is language a method or a theory?" If you're like me, this is a fuzzy question because we are equally bad at appreciating illiteracy (experiencing language as method) and Noam Chomsky (understanding language as theory).

I think one reason SNA battles the method vs theory question so hard is because its devotees are still trying to find a home. Just look again at this picture of "web science" by Tim Berners-Lee and you can see how this proposed paradigm has no single foundation from which to proclaim its theoretical rigor. To my eye, the picture has so many overlapping fields that it actually detracts from Berners-Lee's intention to create "web science."

At the end of this chapter, Kilduff and Tsai recommend further reading, including:

Burt, R.S. 1992. Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.

Granovetter, M. 1973. The strength of weak ties. American Journal of Sociology, 78: 1360-80.

Granovetter, M.S. 1985. Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91: 481-510.

Monge, P.R. and Contractor, N.S. 1999. Emergence of communication networks. In F.M. Jablin and L.I. Putnam (eds), The new handbook of organizational communication: Advances in theory, research, and methods, pp. 440-502. Thousand Oaks, CA: Sage.

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.

Adapt or die: what businesses can learn from science and nature

I just got this invitation to an entrepreneurial networking dinner featuring the talk, "Adapt or die: what businesses can learn from science and nature."

"Adapt or die" made me uneasy, especially when presented as a lesson to be learned "from science and nature," and so I mulled it over long enough to come up with three different reasons to edit the title to "Die and adapt."

1. Nature: Generally speaking, adaptation occurs in a population of organisms, and death occurs in individual organisms. The science of evolution and natural selection is largely about the essential connection between these two phenomena: the death of organisms is a key driving force in the adaptation of populations. Hence, "Die and adapt."
   
2. Science A: Those intellectual smarty-pants in white lab coats are more often wrong than not, and the most common way the wrong ideas get weeded out is when the scientists who believe in them finally die. Then the younger smarty-pants who have slightly better ideas can finally publish, get tenure, and squash the even newer, smarter, generation of upstarts. This scientific soap-opera makes for a tragically hilarious read in Bill Bryson's "A Short History of Nearly Everything." Hence, "Die and adapt."
   
3. Science B: As John Ziman says, "The author [of scientific literature] presents an entirely false picture of his actual procedure of discovery. All the false starts, the mistakes, the unnecessary complications, are hidden; and a yarn, of preternatural prescience, precision and profit, is spun." Keith Sawyer explains the same truth in Group Genius: "Fail early, fail often, fail gloriously." Now just substitute "die" for "fail" to see why we must "Die and adapt."
4. Bonus reason from the world of organizational theory: "When losing is learning"
   

"Adapt or die"--sounds so true, but perhaps it is counterproductive to posit "adapt" and "die" as two alternatives between which we must choose.

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http://webmathematics.net

One more week before classes start. In preparation, I am improving my online Introduction to Network Mathematics, which you will now find at http://webmathematics.net. Along the way I am learning how to be dangerous with Adobe Creative Suite 3 Web Premium, a massively juicy collection of software that I have acquired as a concession to my students, who generally consider set theory and eigenvectors to be dry.

With the URL "http://webmathematics.net," I am tipping my cap to Tim Berners-Lee and gang over at http://webscience.org. Their work can be summed up by the picture at right.

I plan to integrate their "Framework for Web Science" with my own course as much as I can. The fact that they are writing for fellow PhDs and I am teaching non-technical college freshman makes this integration non-trivial, to say the least.

Whenever I get discouraged by the gap I am trying to bridge with this integration, I find encouragement in the words of my new patron saint, John Ziman. The same John Ziman who wrote that "publication of fragments of scientific work may well have been the key event in the history of science" also said:

"In my view the gravest weakness in the organization of modern science is the lack of systematic exposition of the consensus at the stage between [scholarly] review article and the undergraduate textbook."

You can find the above quote in Ziman's 1968 monograph, Public Knowledge ("An Essay Concerning the Social Dimension of Science"), specifically in the chapter "Community and Communications."

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"Information, Communication, Knowledge" by John Ziman

I just finished reading Group Genius by Keith Sawyer, and can't thank Robert Rasmussen of Lego Serious Play enough for recommending this book to me.

My favorite part of Group Genius is the gushing review on the back cover by management guru Ori Brafman (of Starfish and Spider network fame). Brafman says, "Sawyer has completely changed how I think about creativity."

That kind of review buys into the "big light bulb effect" that Sawyer devotes his entire book to dismantling. Instead of big ideas and big light bulbs, Sawyer explains that creativity in fact happens through slow, small, and collaborative steps.

Sawyer's enormous collection of endnotes does a great job of reinforcing his well-spun anecdotes with scholarly empirical research. Even so, I think he understates the degree to which his own book is but a well-packaged echo of work done long before.

Lewis Thomas, in his essay "On societies as organisms" (published in his best-selling 1974 book The Lives of a Cell) quotes John Ziman thus:

"The invention of a mechanism for the systematic publication of fragments of scientific work may well have been the key event in the history of modern science.... A typical scientific paper has never pretended to be more than another little piece in a larger jigsaw--not significant in itself but as an element in a grander scheme. This technique, of soliciting many modest contributions to the store of human knowledge, has been the secret of Western science since the seventeenth century, for it achieves a corporate, collective power that is far greater than one individual can exert."

John Ziman wrote those words in his essay, "Information, Communication, Knowledge," published by Nature in 1969. My favorite part of the essay is the paragraph immediately preceding the part quoted by Thomas:

"Our present system of scientific communication depends almost entirely on [literature with] three basic characteristics: it is fragmentary, derivative, and edited. These characteristics are, however, quite essential."

So, while some people may experience revelation upon reading Group Genius (like Ori Brafman), I instead find Sawyer's book to be fragmentary, derivative and edited. But those are exactly the traits that make Sawyer's book so creative and so worth reading.

BTW, check out Ziman's bibliography and see how he relies on material published in 1939.

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Social networks and organizations: Understanding the research

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

Chapter Two: Understanding Social Network Research. Kilduff and Tsai discuss the scientific ancestry of SNA and highlight the major distinctive concepts of the field. They state their case thus:

"At its best, network research has several distinctive features that differentiate it from traditional approaches in the social sciences:

1. Network research focuses on relations and the patterns of relations rather than on attributes of actors;
2. Network research is amenable to multiple levels of analysis, and can thus provide micro-macro linkages;
3. Network research can integrate quantitative, qualitative, and graphical data, allowing more thorough and in-depth analysis.

None of these features is well established in traditional approaches in the social sciences."

The authors present Exhibit A: Bruce Kapferer's analysis of strategy and transaction in an African factory. Their use of this case study is notable for a few reasons:

• It was published in 1972 and so defuses the trendy stigma of ONA
• Kapferer's study begins with a preface by his mentor J. Clyde Mitchell, who says, "Kapferer himself has argued cogently that social networks do not by themselves constitute a 'theory'. ... He must go beyond these data for an adequate explanation of the events he is considering." This defuses the network zealotry mention by Kilduff and Tsai in their own book's introduction, and sets up their next chapter: "Is there social network theory?"
• The actual story observed by Kapferer involves the emergence of organized labor in one particular African factory. As the story opens, the workers are too decentralized to influence management. By the end, the workforce is much more centralized and successfully organizes a strike against the factory owners. I can almost hear Kilduff and Tsai snickering at the thought of management consultants trying to use this case study to sell SNA to some CEO. Everything about the story is backwards from the way we commonly preach networks and collaboration today.
• The analysis by Kapferer is both strikingly thorough and strikingly ignorant of related research that is easy to see with hindsight. I guess scientists are only human after all.

At the end of this chapter, Kilduff and Tsai recommend further reading, including:

Nohria, N. and Eccles, R.G. (eds). 1992. Networks and organizations: Structure, form and action. Boston, MA: Harvard Business School Press. A broad cross-sectional collection of SNA work.

Scott, J. 2000. Social network analysis: A handbook. 2nd edn. Newbury Park, CA: Sage. The best SNA handbook available.

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Master of the universe

Doing nothing has given me time to re-read Chapter 2 of Social Networks and Organizations, which I will discuss in a blog post soon. The chapter draws heavily on one particular case study, Strategy and transaction in an African factory, by Bruce Kapferer. This is no ordinary case study, but in fact a 366-page book published in 1972.

Doing nothing has given me so much time that I tracked this book down. The effort was definitely worth it--it was my first time checking out a book with my BU faculty ID. I get to keep the book for five months. I used to think three weeks with the option to renew was powerful. Now I am master of the library universe. I may not read Kapferer for four months or so, just to enjoy thinking about the poor students who are waiting to get their hands on his book.

Those of you with less than omnipotent library powers can take some solace in the knowledge that there is not one picture or map in Kapferer's entire 366 pages of exemplary network analysis. Instead, he presents the African factory network in matrix form, like so:
And, there are only two such networks presented in the whole book: one "before" and one "after." Click on the image above to see more detail and re-live 1970s typesetting.

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.

Doing nothing

I am on a mini-hiatus, defined most notably by ignoring my email.

Reading Tao Te Ching provides lots of moral support for doing nothing. The world will survive a few days of my not fixing its problems.

Not everyone likes this philosophy. Here in Massachusetts, a new law that mandates health insurance for all residents has been widely publicized on TV with the catch-phrase: "Ignoring a problem never made one go away."

Those seeking a counter-argument to this credo should read Lewis Thomas (Dean of Yale Medical School, etc., etc.) who in Lives of a Cell wrote an essay "Your Very Good Health," which he summed up near the end by saying, "The great secret... is that most things [i.e., health problems] get better by themselves."

Digging up Thomas' classic collection of essays has given me lots of other food for thought that I can share later, when I am back to doing something.

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Memorable KM advice from Lao Tsu

Inspired by Zen Motorcycles, I am reading Tao Te Ching for the first time. Paraphrasing chapters one and two, here is

Lao Tsu's guide to Knowledge Management:

ONE

The name that can be named is not the eternal name.
Without desiring names, one can see the mystery.
Ever desiring names, one can see manifestations.

TWO

Therefore the sage goes about doing nothing, teaching no-talking.
The ten thousand things rise and fall without cease,
Creating, yet not possessing,
Working, yet not taking credit.
Work is done, then forgotten.
Therefore it lasts forever.

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Fat people of the world, unite!

The New England Journal of Medicine reported last week that obesity spreads through social networks. After the initial wave of headlines, I checked it out and was really impressed by the research, done by Nicholas A. Christakis and James H. Fowler.

One small (but to me significant) jewel of their research is the network movie they use to demonstrate their findings. See Judy Breck's post on SmartMobs for a link to the movie, which uses state-of-the-art network visualization algorithms to present complex data in an intuitive way. The authors used SoNIA to make the movie. (More on network movies here.)

Another gem accompanying this study is the editorial by Albert-Laszlo Barabasi. He points out the multiple levels of networks at play, which include not just social networks but also biochemical networks. Compare Barabasi's diagram to Peter Gloor's multi-level analysis of business innovation (at right) and you can see why there is actual scientific substance behind work such as Verna Allee's which "uses principles of living systems theory to help companies evolve management thinking."

Publicity like this makes it all too easy to go overboard with network science, however. Breck's otherwise solid post concludes, "A key truth the article demonstrates is that social networking is not some oddball result of the emergence of the Internet. Networking principles are deeply imbedded [sic] into the physical and psychological venues, as well as the virtual." I feel rather confident that the authors of the study would be puzzled by Breck's statement; a scan of their paper shows that they are confident enough in the foundations of network principles to use them in their work without any need to get defensive about their analysis.

What strikes me most about the study is the off-hand comment at the very end of the movie. We naturally expect obesity and non-obesity to cluster, and they do; however, as the authors note, we also see that obesity takes over the core of the social network and non-obesity moves to the periphery: Is this significant? My hunch is yes, and that there are implications for our health in this core-periphery structure.

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.

Social networks and organizations: Introduction

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

Introduction: Kilduff and Tsai see a big future for SNA, as long as its more zealous fans can get out of their own way.

They say, "The potential application of the social network approach to organizations is, in our view, enormous. The full spectrum of organizational phenomena that network thinking can illuminate extends across levels from micro to macro, and includes topics typically covered in fields such as organizational cognition, organizational behavior, organizational theory, and strategic management."

However, "The network paradigm is in danger of becoming a victim of its own success--invoked by practically every organizational researcher, included in almost every analysis, and yet strangely absent as a distinctive set of ideas."

Kilduff and Tsai do not flinch from drawing a line between successes and failures in SNA research to date. In particular, they comment that effective formation of networks clearly helps individuals; however, "the jury is still out as to whether social capital measured at the individual level does indeed have effects at the community level." In other words, SNA can help me get promoted faster, but does that help my company as a whole?

At the end of each chapter, Kilduff and Tsai recommend further reading. The references at the end of this chapter are a collection of all-round MVPs:

Baker, W.E. 2000. Achieving success through social capital, San Francisco: Jossey-Bass. A practitioner-oriented guide.

Baker, W.E. and Faulkner, R.R. 2002. Interorganizational networks. In J.A.C. Baum (ed.), The Blackwell companion to organizations, pp. 520-40. Oxford: Blackwell. A survey of research.

Brass, D.J. 1995. A social network perspective on human resources management. In gerald R. Ferris (ed.), Research in personnel and human resources management, 13: 39-79. Greenwhich, CT: JAI Press. A guide to SNA for HR.

Krackhardt, D. and Brass, D.J. 1994. Intraorganizational networks: The micro side. In S. Wasserman and J. Galaskeiwicz (eds), Advances in social network analysis, pp. 207-29. Thousand Oaks, CA: Sage. Survey of SNA research for leadership development and other work attitudes.

Powell, W.W. and Smith-Doerr, L. 1994. Networks and economic life. In N.J. Smelser and R. Swedberg (eds), The handbook of economic sociology, pp. 368-402. Princeton, NJ: Princeton University Press. Broad survey of research on topics such as power, communication networks, and networks of production.

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Using SNA to enhance collective leadership

In July of 2006, the Leadership Learning Community (LLC) awarded a Community Seed Fund grant to explore the use of social network analysis (SNA) and assess its value to those who run, study, and fund leadership development programs.

Our experience using SNA in three communities led to three strikingly different results. The table below summarizes the results in each community along with important distinguishing traits of each community:

Community	Primary result of SNA	Traits of community
Schuylkill Learning Community	Collective leadership: Community members improved their “big picture” awareness of who is working with whom how they can accomplish more together.	Funder-initiated community with formal membership, paid external facilitator and mandatory attendance at meetings.
North Carolina Community Solutions Network	Professional perspective: Community members learned to see their work outside the community in a new way. Community facilitator improved “big picture” awareness.	Self-organized community with paid internal facilitator, predominantly rural constituency
Bay Area LLC Learning Circle	Introduction to new topic: Community members expressed interest in learning more about SNA technology, methods, and applications.	Self-organized community with no formal facilitator, next door to Silicon Valley

In other words, communities under formal oversight gained a lot from seeing themselves through a network lens (instead of the usual "us" vs "the man" lens), while more self-energized communities found the network lens to be less transformational (but still academically interesting).

You can read our complete report here, where it lives with other Shared Knowledge and Resources provided by LLC.

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Best SNA book ever

I just put down my new most favorite SNA book: Social Networks and Organizations, by Martin Kilduff and Wenpin Tsai.


What's so great about this book? Here are a few of my faves:

• Cover. (See above, and note how well it goes with my favorite colors.)
  
• Content. In the authors' words, "This book reflects our own view of what is important in social network research. Instead of providing just a review of existing research, we have opened up dialogue on a range of new approaches." Then they continue: "We think that debate and controversy are good for social science in that they encourage a more rapid development of theory and research."
• Readability. As the authors soar over the highlights of social network theory, they trace a clear historical arc with a wry tone that adds up to an academic page-turner. Along the way, they indulge my weakness for polite but merciless deflation of all puffed-up hot-air-bags in sight. I couldn't put it down!

I like this book so much that I am going to devote a series of posts to it. Each post will focus on a chapter. The chapters include:

1. Introduction
2. Understanding social network research
3. Is there social network theory?
4. Bridging the gap between structuralist and individualist approaches
5. Goal-directed and serendipitous network processes
6. Towards a poststructuralist network approach
7. Conclusion

As I write about each chapter, I will update the list above so that it links to the appropriate posts.

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.

Social network metrics are for eggheads

Thanks to Frank van Ham, co-creator of ManyEyes and researcher at IBM's Visual Communication Lab, for giving me the title of my next talk: "Social network metrics are for eggheads." I don't know where or when this talk will occur, but that will be the title.

One case study I will share as part of the talk will be the the following wonderful project by Valdis Krebs. He was working with a group advocating for healthy affordable housing. Time and again they had cornered one dilapidated building or another into cleaning up its lead paint, only to hear the owners announce, “We just sold the building, sorry.” After hearing this excuse a few times, they sensed something was up. With a little research at the library, they found all the real estate holding companies involved in the series of sales were associated with one “godfather” who ran the whole empire through a network of children, siblings, spouses, and in-laws. The piece of evidence that ultimately convinced the judge to throw the book at the lead-paint mafia was a network map like so:
Real estate companies are green at the top, godfather/mother are at the very bottom, and intermediaries who own/run the companies are in the middle.

Those of us participating in the debate over how best to measure social networks in ways that predict important outcomes (such as revenue, profit, or shareholder value, to name a few) can forget that there are also simpler and less controversial applications of SNA. Valdis' Family Ties picture above is one great example.

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.

Guide to effective network survey design by Terry Murphy

Terry Murphy has written an outstanding (and sobering) assessment of online network survey tools. He has very kindly allowed me to post his report. The abstract is below:

This research report takes questionnaire usability best practice and online usability in a questionnaire context as the basis for developing usability guidelines for social network analysts who choose to employ online modes of data acquisition.

Highlighting the distinct character of network analysis questionnaires, and the opportunities, and potential cost efficiencies, of acquiring network analysis data through online networks, the relationship between usability and communication in the context of questionnaires, particularly the importance of visual design, is considered.

The report provides eleven usability guidelines for online network analysis questionnaires ranging from the general to the network-analysis specific.

The guidelines are not new or revolutionary, but rather provide a network analysis-specific overlay to general online questionnaire guidelines.

Three freely-available, low-cost online survey applications are assessed against the guidelines.

You can get the full report here.

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.

Harnessing the power of networks

In celebration of the third birthday of Connectedness (officially Friday but I can't wait that long) and in tribute to the power of networks, I want to honor the dozens and dozens of unhappy Sears customers who have turned my blog into the #1 Sears customer relations website. That's right, if you Google "Sears customer relations" there is only one website ranked higher than mine, and it's the Sears Investor Relations site--a mistake, clearly--so I like to think that my site is the true #1 Sears customer relations site on the Internet.

How did this happen? It's all about "harnessing the power of networks," baby! One day I posted a story about a bum refrigerator (with a happy ending no less) and three years later that post has risen through the Google ranks, gathering comments with ever-increasing network power. Now I am host to 31 tales of Sears customer woe (and counting). By the way, that does not include the emails I get regularly on this topic.

Within this experience is an important lesson in the reality of "harnessing the power of networks." No matter how well I learn this lesson, though, it fails to soften the sting of knowing that my broken-refrigerator story is the most popular page on my blog by a factor of two. This does not feel like it is burnishing my SNA-guru credentials.

Still, it could be worse. Thanks, everybody, for not posting anything illegal on my site (unlike Digg).

Connectedness year #4 promises to be our best, most network-harnessed yet!

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.

New network movie algorithm

Shortly after hearing my "Complaining about Network Visualization" talk at the University of Maryland, host Ben Shneiderman tipped me off to this award-winning paper presented at EuroVis 2007, where he had just given the keynote address. Ben's tip directly addresses my complaint that most SNA tools consider visualization as an isolated problem to be done once. The problem is that we like to look at networks change over time and watch series of network maps. Generating series of network maps that communicate meaningful information is no easy task. The movie below by Yaniv Frishman and Ayellet Tal summarizes their significant step forward in automating this task. (15MG QuickTime)
Here's an overview of my numerous related posts on network visualization.

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.

LINKS: Intra Organizational Networks in Business

Big news from the Gatton College of Business and Economics at the University of Kentucky, which very recently founded LINKS, the International Center for Social Network Research. The homepage says

The mission of the center is to bring together scholars from different academic disciplines who share a common interest in social network research and application. Our overarching goal is to conduct and publish cutting-edge research in the rapidly expanding field of social network analysis; and to serve as a bridge between the science of social networks and real world organizational problems. The core insight of the network perspective is that the pattern of relations among the elements of a system—be they people, organizations, neurons, or computer servers—has important consequences for the system's performance.

Some very impressive people have moved to Kentucky to join Dan Brass, including Steve Borgatti, Joe LaBianca, and Ajay Mehra. Keep your eye on LINKS.

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.

Qualitative Data, Quantitative Analysis

Pacey Foster (soon to be professor in the School of Management at UMASS Boston) points me to this essay by H Russell Bernard, "Qualitative Data, Quantitative Analysis." It presents a balanced overview that takes the qualitative vs quantitative discussion into separate dimensions of data and analysis. So instead of provoking an argument that pure number-crunching cannot completely describe multi-cultural organizational effectiveness (as I did yesterday), Bernard invites consideration of the following two by two matrix:
What does this mean for analyzers of social networks? We work in all four cells. In my experience we tend to move through them roughly in sequence:The only real beef I have with Bernard's discussion is where he says, "strictly speaking, there is no such thing as quantitative analysis of qualitative data." Since he wrote the essay in 1993, before the "digital revolution," I will let him off the hook for suggesting such an impermeable boundary between these two.

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.