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Yes, that is exactly how you find k-cores, except now they are naming each set of iteratively pruned nodes as "shells" and... the final shell is the core! Like I said maybe this has academic significance...

I agree with their conclusion -- it's not node degree that matters, but node "location, location, location" [the same "golden rule" as in Real Estate]

Valdis

On Feb 3, 2010, at 6:39 PM, Brian Keegan wrote:

> ***** To join INSNA, visit http://www.insna.org ***** Their arxiv link to their paper is here:
> 
> http://arxiv.org/PS_cache/arxiv/pdf/1001/1001.5285v1.pdf
> 
> "K-shell decomposition method" is described on page 19.
> 
> On Wed, Feb 3, 2010 at 5:22 PM, Valdis Krebs <[log in to unmask]> wrote:
> *****  To join INSNA, visit http://www.insna.org  *****
> 
> Yes Carl, agree, and how is a "k-shell" different than a "k-core" [allowing for multiple components/cores]?
> 
> Overall I agree with what they say, but am left with the feeling, "so what is so new and unique here?"  Of course I am a practitioner first, so maybe I am overlooking some academic definitions here?
> 
> Valdis
> 
> 
> On Feb 3, 2010, at 6:03 PM, Carl Nordlund wrote:
> 
> > *****  To join INSNA, visit http://www.insna.org  *****
> >
> > quoting: "The question then is how to find these influential individuals. Kitsak and co say that the way to do this is to study a quantity called the network's "k-shell decomposition". That sounds complicated but it isn't: a k-shell is simply a network pruned down to the nodes with more than k neighbours. Individuals in the highest k-shells are the most influential spreaders."
> >
> > Although I might misunderstand what they mean with "pruned down", what's the difference between this method and the various ways to identify subgroups as based on nodal degrees? I don't understand the novelty of this proposed method - perhaps someone can enlighten me? Furthermore, what happened to the influence- and betweenness-based measures of centrality? I doubt most network analysts equate the "most influential spreaders" in a social network with those that have the most number of connections (i.e. dichotomized degree centrality) in light of the more "recursive" ways for calculating actor centrality.
> >
> > Yosem Companys wrote:
> >> ***** To join INSNA, visit http://www.insna.org ***** *Best Connected Individuals Are Not the Most Influential Spreaders in Social Networks*
> >> /Technology Review (02/02/10)/
> >> Boston University (BU) researchers have developed a method for studying and identifying hubs within social networks. The approach emphasizes the location of the individual within the network as opposed to the number of connections. "In contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or the most central people," says BU's Maksim Kitsak. The researchers found that if a hub exists at the end of a branch it will have a minimal impact on the core of the network. However, a less connected person strategically placed in the core of a network can have significant effects that lead to dissemination through a large fraction of the population, Kitsak says. By studying a quantity called the network's k-shell decomposition, researchers can locate these specially placed individuals, which is the key to understanding the dynamics of a network.
> >> View Full Article <http://www.technologyreview.com/blog/arxiv/24748/?a=f>
> >> _____________________________________________________________________ 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.
> >
> >
> > --
> > Carl Nordlund, BA, PhD student
> > carl.nordlund(at)hek.lu.se
> > Human Ecology Division, Lund university
> > www.hek.lu.se
> >
> > _____________________________________________________________________
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> _____________________________________________________________________
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> 
> 
> -- 
> Brian C. Keegan
> Ph.D. Student - Media, Technology, & Society
> School of Communication, Northwestern University
> 
> Science of Networks in Communities, Laboratory for Collaborative Technology, Center for Technology & Social Behavior
> _____________________________________________________________________ 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.

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