Scale Free

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There's a good chance, though, that in a scale-free network, many transactions would be funneled through one of the well-connected hub nodes - one like Yahoo Inc. Because of these differences, the two types of networks behave differently as they break down. The connectedness of a randomly distributed network decays steadily as nodes fail, slowly breaking into smaller, separate domains that are unable to communicate. Scale-free networks, on the other hand, may show almost no degradation as random nodes fail. With their very connected nodes, which are statistically unlikely to fail under random conditions, connectivity in the network is maintained.

It takes quite a lot of random failure before the hubs are wiped out, and only then does the network stop working. Of course, there's always the possibility that the very connected nodes would be the first to go. In a targeted attack, in which failures aren't random but are the result of mischief, or worse, directed at hubs, the scale-free network fails catastrophically.

Take out the very connected nodes, and the whole network stops functioning. In these days of concern about cyberattacks on the critical infrastructure, whether the nodes on the network in question are randomly distributed or are scale-free makes a big difference.

Scale-Free Networks: A Decade and Beyond | Science

Until now, it has been accepted that stopping sexually transmitted diseases requires reaching or immunizing a large proportion of the population; most contacts will be safe, and the disease will no longer spread. But if societies of people include the very connected individuals of scale-free networks—individuals who have sex lives that are quantitatively different from those of their peers—then health offensives will fail unless they target these individuals.

These individuals will propagate the disease no matter how many of their more subdued neighbors are immunized.


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Now consider the following: Geographic connectivity of Internet nodes is scale-free, the number of links on Web pages is scale-free, Web users belong to interest groups that are connected in a scale-free way, and e-mails propagate in a scale-free way. Barabasi's model of the Internet tells us that stopping a computer virus from spreading requires that we focus on protecting the hubs.

See additional Computerworld QuickStudies. Here are the latest Insider stories. More Insider Sign Out. Sign In Register. Sign Out Sign In Register. Latest Insider. Check out the latest Insider stories here. More from the IDG Network. In future work on specific subgroups of networks, a domain-specific weight scheme could be used with the evaluation criteria described here. The accuracy of the fitting, comparing, and testing methods, and the overall evaluation scheme itself, were evaluated using four classes of synthetic data with known structure.

Three of these generated networks that contain power-law degree distributions: a directed version of preferential attachment 79 , a directed vertex copy model 21 , and a simple temporal power-law random graph. Journal peer review information : Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Albert, R. Diameter of the World-Wide Web. Nature , — Biological network comparison using graphlet degree distribution.

Bioinformatics 23 , — Lima-Mendez, G. The powerful law of the power law and other myths in network biology. Mislove, A.

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Measurement and analysis of online social networks. Agler, M. Microbial Hub Taxa Link Host and abiotic factors to plant microbiome variation. PLoS Biol.

Scale-free networks are rare

Ichinose, G. Invasion of cooperation in scale-free networks: accumulated versus average payoffs. Life 23 , 25—33 Zhang, L. Exactly scale-free scale-free networks.

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A , — Dorogovtsev, S. Evolution of networks. Emergence of scaling in random networks. Science , — Willinger, W. Mathematics and the internet: a source of enormous confusion and great potential. AMS 56 , — Pastor-Satorras, R. Epidemic dynamics in finite size scale-free networks. E 65 , Error and attack tolerance of complex networks. Carlson, J. Highly optimized tolerance: a mechanism for power laws in designed systems. E 60 , — Newman, M. Mitzenmacher, M. A brief history of generative models for power law and lognormal distributions.

Internet Math.

Network Structure

Goh, K. Classification of scale-free networks. Natl Acad.


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  4. USA 99 , — Epidemic processes in complex networks. Epidemic spreading in scale-free networks. Aiello, W. A random graph model for massive graphs. A random graph model for power law graphs. Spread of epidemic disease on networks. E 66 , Lee, D. Synchronization transition in scale-free networks: clusters of synchrony. E 72 , 1—6 Restrepo, J. Onset of synchronization in large networks of coupled oscillators. E 71 , 1—12 Ichinomiya, T. Frequency synchronization in a random oscillator network. Synchronization in large directed networks of coupled phase oscillators.

    Chaos 16 , Emergence of synchronization in complex networks of interacting dynamical systems. Price, D. Networks of scientific papers. Simon, H. On a class of skew distribution functions. Biometrika 42 , — Evolving protein interaction networks through gene duplication. Berger, N. Leskovec, J. Graph evolution. ACM Trans.

    Data 1 , 1—41 Gamermann, D. A comprehensive statistical study of metabolic and protein-protein interaction network properties. House, T.