***** To join INSNA, visit http://www.insna.org ***** The (degree) distribution underlying the figure depicted in Barabasi's article (the one Valdis drew our attention to) follows a "scale free" (or power law) distribution. I've found that the degree distributions in the organizational samples I've collected more closely resemble the normal distribution; but the distribution of betweenness centrality does resemble the power law distribution that Barabasi, Watts and others have found (across a remarkable range of networks, from the nervous system of one-millimeter worm C. Elegans to the electricity network of the Western United States). Also, in the organizational samples I’ve examined, the distribution of degree centrality in certain types of networks (e.g., “perceived leadership” network) follow the power law distribution, but the distribution of degree centrality in other networks (e.g., the trust network) does not. To understand why “human” networks may in some cases follow scale free distributions and normal distributions in others will probably require close attention to the theoretical logic that is used to explain the dynamic growth (and decay) of these networks. Barabasi and colleagues have focused on “growth” (the periodic addition of nodes to a network over time, starting with the nucleus) and “preferential attachment” (the idea that new nodes prefer to connect with nodes that have more ties rather than with nodes with fewer ties) as the twin engines of network dynamism. These principles make sense in many systems. But they surely don’t hold for all systems: In friendship networks, for example, nodes may prefer to connect with similarly connected nodes rather than with the best connected ones. Not everyone reaches out to the stars. One reason that physicists like Barabasi are so excited about the “new” science of networks is that it holds out the promise for general laws of networks that would apply across all kinds of networks. Those studying organizational networks have argued for some time that this quest for general laws may be misguided. Still, I for one say let’s not dampen the physicists enthusiasm just yet. Even if their quest for universal laws of networks proves unattainable, they are sure to throw up marvelous insights along the way. Ajay Mehra University of Cincinnati -----Original Message----- From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Steven Corman Sent: Wednesday, February 02, 2005 10:22 AM To: [log in to unmask] Subject: Re: All Networks Look the Same? ***** To join INSNA, visit http://www.insna.org ***** Three thoughts... (1) These (valid) critiques notwithstanding, let us acknowledge that it is interesting if there is some kind of fractal quality to large networks. (2) One wonders what characteristic might distinguish networks that are self-similar from those that aren't. Is it social vs. physical networks as Tom implies? Or a function of size as Bettina suggests? If size, what's the tipping point? (3) INSNA members seem not to be part of the networks of paper reviewers and science reporters who are creating this "new" science of networks. Why is that? Best, Steve ________________________________________________ Steven R. (Steve) Corman Professor, Hugh Downs School of Human Communication Arizona State University http://www.public.asu.edu/~corman Chair, Organizational Communication Division International Communication Association http://www.icahdq.org -----Original Message----- From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Bettina Hoser Sent: Wednesday, February 02, 2005 7:09 AM To: [log in to unmask] Subject: Re: [SOCNET] All Networks Look the Same? ***** To join INSNA, visit http://www.insna.org ***** Hi This self-similarity idea has been around as it seems for a while. I just checked my own (limited) literature base and found an article from 2002 http://arxiv.org/pdf/cond-mat/0211498 which was released in Physical Reveiw letter E. As of now I do not have hands-on experience if this is really true, but it´s not a new idea. But just as a philosophical thought: once the network - any network - gets big enough isn´t it somehow quite likely to become self-similar? Best regards, Bettina -- Dipl.-Phys. Bettina Hoser Informationsdienste und elektronische Märkte Fakultät für Wirtschaftswissenschaften Universität Karlsruhe (TH) D-76128 Karlsruhe Gebäude 20.20 RZ (Raum 164), Zirkel 2 Telefon: +49.721.608-8407 Telefax: +49.721.608-8403 [log in to unmask] http://www.em.uni-karlsruhe.de _____________________________________________________________________ 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. _____________________________________________________________________ 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. _____________________________________________________________________ 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.