I am reading Linked, The New Science of Networks, by Albert-László Barabási. I'm about halfway through and finding it more readable than The Six Degrees of Separation, that covered essentially the same material.

In the late sixties, Mark Granovetter, a grad student at Harvard, explored social networking by asking residents of Newton, Massachusetts, how they found their jobs. He was surprised to find that close friends played a much less important role than people who were only weakly connected socially. In fact, close friends were no help at all.

Think about it. You have a handful of really close friends, and you travel in the same circles and know most of the same information. Not much chance of finding something new in this small, tightly-knit crowd.

However, each of your friends has friends in other groups. These acquaintances are bathed in different streams of information.

And since each contact opens up bonds to another group of friends, the number of friends of friends and so forth grows extremely rapidly:

Granovetter's paper describing weak ties was rejected by American Sociological Review and languished for several years before being recognized as one of the most influential papers in modern sociology. It's the weak ties transmit new ideas from the outside world.

The weak ties hypothesis helped researchers see through the oversimplifications that had hamstrung their study of networks.

  • Networks (information networks as well as human networks) are not random. Clustering is common. The 80/20 rule applies. They follow Power Laws, not the normal distribution.
  • Not all nodes are created equal. Many have multiple connections. "Connectors" may have hundreds; they cross disciplines and are part of many circles (think Tipping Point).
  • Unlike the mathematician who starts with and studies a set numer of nodes, the real world contains growing networks that start small but grow rapidly, link-by-link.
  • I haven't gotten to it in the book, but of course, not all pipes are the same. Consider: bandwidth, reciprocity, social convention, trust, signal:noise, and reputation. Our representation of a network as a group of equivalent nodes connected by standard links now seems naive.

    Google came late to the party but soon proved that "first mover advantage" is a myth. Google was sticky. In network lingo, Google was fit, not unlike people in social networks who make every contact a lasting tie. "Beauty before age."

    Another grad student, Ginestra Bianconi, discovered that the web behaves according to some of the laws of quantum mechanics. Researchers had looked at network phenomena as a math problem or geometry exercise. No, it's more like a complex system. This means that sometimes it's winner take all (think of Microsoft in the software ecosystem). I don't think it's mere coincidence that grad students are making these discoveries rather than faculty members who've spent decades in the math department.

    Robustness is a measure of stability and survival under extreme conditions and Murphy's Law. A tightly interwoven system exhibits this fault-tolerance. You could wipe out 80% of the nodes on the Internet and it would continue to function.

    Network thinking is poised to invade all domains of human activity and most fields of human inquiry. It is mroe than another helpful perspective or tool. Networks are by their very nature the fabric of most complex systems, and nodes and links deeply infuse all strategies aimed at approaching our interlocked universe.

    The author ends with an analogy to Christo's wrapped Reichstag. Networking has been under a shroud too long. It's time to unwrap it.

    Yuck. I was looking for more. This reminds me of my undergraduate major, Sociology. I chose this social science because I thought I'd learn something scientific. To my disappointment, the field turned out to be primarily social. In fairness, I think the difficulty is that we simply don't know much abuot network behavior yet. The upside is that I feel that I have a shot at expanding our understanding of it.

    Posted by Jay Cross at August 1, 2003 12:39 AM | TrackBack

Each time I read something on knowledge networks I'm reminded of James Burke's PBS Series "Connections" and how seemingly disparate events in history are linked. His new project looks like it is going to be a pretty cool way to learn if it functions the way it is described (

I do wonder if the coming Network Thinking will make the human learning process more efficient or will the massive volume of information and the limitless paths from each node negate any potential efficiencies? I look at my own education on e-learning as an example. As I continue to seek out additional information on the subject, I'm led to related areas which in turn are linked to related areas, which in turn...well, you get the idea. It is very easy to go off on a tangent that was unintended, but what if that next great "a-ha!" breakthrough is on one of these detours? The challenge is that there are now more "detours" (nodes) than ever before.

Posted by: Mark Vay at August 5, 2003 12:16 PM

Take a look at my review of Linked on Bookshelved:

The interesting to note is the bit about the difference between attack-resistance and failure-resistance. This is the point of chapter 9. The internet may be safe from failures which take out a random 80% but if you 'take out' a small number of 'hubs' the whole system fails.

For some reason people always overlook this aspect of scale-free networks.

Posted by: ade at August 23, 2003 11:56 AM

Take a look at my review of Linked on Bookshelved:

The interesting to note is the bit about the difference between attack-resistance and failure-resistance. This is the point of chapter 9. The internet may be safe from failures which take out a random 80% but if you 'take out' a small number of 'hubs' the whole system fails.

For some reason people always overlook this aspect of scale-free networks.

Posted by: ade at August 23, 2003 11:57 AM

i like it

Posted by: paris hilton slip at June 29, 2004 01:26 AM

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