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Subject: Re: another scientist discovers 6 degrees
From: Barry Wellman <[log in to unmask]>
Reply-To:Barry Wellman <[log in to unmask]>
Date:Wed, 7 Sep 2005 21:06:03 -0400
Content-Type:TEXT/PLAIN
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TEXT/PLAIN (169 lines)


*****  To join INSNA, visit http://www.insna.org  *****

hey, my project is about 50% gay. We have the edge on homo stuff.

 Barry
 _____________________________________________________________________

  Barry Wellman         Professor of Sociology        NetLab Director
  wellman at chass.utoronto.ca  http://www.chass.utoronto.ca/~wellman

  Centre for Urban & Community Studies          University of Toronto
  455 Spadina Avenue    Toronto Canada M5S 2G8    fax:+1-416-978-7162
             To network is to live; to live is to network
 _____________________________________________________________________


On Wed, 7 Sep 2005, Miller McPherson wrote:

> Date: Wed, 7 Sep 2005 18:03:43 -0700 (MST)
> From: Miller McPherson <[log in to unmask]>
> To: Barry Wellman <[log in to unmask]>
> Cc: [log in to unmask]
> Subject: Re: another scientist discovers 6 degrees
>
> Well,
>      It's good to see that the computer scientists have invented homophily
> too...
> Miller
>
> On Wed, 7 Sep 2005, Barry Wellman wrote:
>
> > *****  To join INSNA, visit http://www.insna.org  *****
> >
> > A U Mass Amherst press release, courtesy of ACM TechNet
> > Like almost all other write-ups, they do not note that the great majority
> > of networks did Not reach the target.*
> >
> > Indeed, when I replicated via email with my cousin Lloyd** in Calif a few
> > years ago, I gave up when I realized how few were reaching the target.
> >
> > But then again I am not a computer scientist or a physicist!
> >
> > Notes:
> >
> > *Thanks to Charles Kadushin for pointing this out.
> >
> > ** The dropouts missed a wonderful guy and a hunk who may be on The
> > Bachelor this year.
> >
> >  Barry
> >  _____________________________________________________________________
> >
> >   Barry Wellman         Professor of Sociology        NetLab Director
> >   wellman at chass.utoronto.ca  http://www.chass.utoronto.ca/~wellman
> >
> >   Centre for Urban & Community Studies          University of Toronto
> >   455 Spadina Avenue    Toronto Canada M5S 2G8    fax:+1-416-978-7162
> >              To network is to live; to live is to network
> >  _____________________________________________________________________
> >
> > “Six Degrees of Separation” Theory Explained in New Algorithm by UMass
> > Amherst Researchers
> >
> > Sept. 6, 2005
> >
> > Contact:        Rachel Ehrenberg
> > 413/545-0444
> >
> > AMHERST, Mass. – University of Massachusetts Amherst researchers have
> > invented a new algorithm that solves a network-searching conundrum that
> > has puzzled computer scientists and sociologists for years.
> >
> > The scientists created an algorithm that helps explain the sociological
> > findings that led to the theory of “six degrees of separation,” and could
> > have broad implications for how networks are navigated, from improving
> > emergency response systems to preventing the spread of computer viruses.
> >
> > Dubbed expected-value navigation, the algorithm describes an efficient way
> > of searching a particular class of networks and was presented by doctoral
> > student Ozgur S,ims,ek, and David Jensen, professor of computer science,
> > at the 19th International Joint Conference on Artificial Intelligence in
> > Edinburgh, Scotland.
> >
> > The algorithm is applicable to a number of networks say the researchers.
> > Ad-hoc wireless networks, peer-to-peer file sharing networks and the World
> > Wide Web are all systems that could benefit from more efficient
> > message-passing. The algorithm could work especially well with dynamic
> > systems such as ad-hoc wireless networks where the structure may change so
> > quickly that a centralized hub becomes obsolete.
> >
> > The work was inspired by research pioneered in the late 1960s that focused
> > on navigating social networks, explains S,ims,ek. In a now famous study by
> > psychologists Milgram and Travers, individuals in Boston and Omaha, Neb.,
> > were asked to deliver a letter to a target person in Boston, but via an
> > unconventional route: the message had to be passed through a chain of
> > acquaintances. The people starting the chain had some basic information
> > about the target individual—including name, age and occupation—and were
> > asked to forward the letter to someone they knew on a first-name basis in
> > an effort to deliver it through as few intermediaries as possible. Of the
> > letters that reached the target, the median number of people in the
> > message-passing chain was a mere six.
> >
> > “What came out of that study was that we are all connected,” says
> > S,ims,ek. But the findings also raised a number of questions about how we
> > are connected, she says. What are the properties of these networks and how
> > do people efficiently navigate them?
> >
> > The social network exploited by Travers and Milgram isn’t a
> > straightforward, evenly patterned web. For one thing, network topology is
> > only known locally—individuals starting with the letter did not know the
> > target individual—and the network is decentralized—it didn’t use a formal
> > hub such as the post office. If navigating such a network is to
> > succeed—and tasks such as searching peer-to-peer file sharing systems or
> > the navigating the Web by jumping from link to link do just that—there
> > must be parts of the underlying structure that successfully guide the
> > search, argue Jensen and S,ims,ek.
> >
> > Participants in the Travers and Milgram study who efficiently sent the
> > message probably acted intuitively by combining two human traits that
> > apply to computerized network-searching as well, say the researchers.
> > People tend to associate with people who are like themselves, and some
> > individuals are more gregarious than others. “Searching” using both of
> > these factors, one can efficiently get to a target even when little is
> > known about the network’s structure.
> >
> > The tendency of like to associate with like, or homophily, means that
> > attributes of a node—an individual in the Travers and Milgram study—tend
> > to be correlated. Bostonians often know other Bostonians, and the same
> > holds true for qualities such as age or occupation. The second important
> > characteristic of these networks is that some people have many more
> > acquaintances than others. This “degree disparity” leads to some
> > individuals acting as hubs.
> >
> > Taking these factors into account simultaneously results in a searching
> > algorithm that gets messages to the target by passing it to gregarious
> > individuals who are most like the target. Or in the language of
> > network-searching, it favors nodes that maximize the probability of
> > linking directly to the target, which is a function of both degree and
> > homophily, say the scientists.
> >
> > Previous research had explored these aspects separately, but S,ims,ek and
> > Jensen are the first to step back and incorporate both these qualities
> > into one broadly applicable algorithm with a strong basis in probability
> > theory. And the combination yields a powerful punch. It is remarkably
> > efficient at finding the short paths between nodes without knowing the
> > central network’s structure, say the researchers
> >
> > “In this case, one plus one is more than two,” says S,ims,ek.
> >
> > _____________________________________________________________________
> > SOCNET is a service of INSNA, the professional association for social
> > network researchers (http://www.insna.org). To unsubscribe, send
> > an email message to [log in to unmask] containing the line
> > UNSUBSCRIBE SOCNET in the body of the message.
> >
>
> ********************************        *
> Miller McPherson                     *  *
> Professor of Sociology               ******
> University of Arizona                   *
> [log in to unmask]                  *
> ********************************        *
>

_____________________________________________________________________
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