Réka Albert
Distinguished Professor of Physics and Biology at Pennsylvania State University; co-author, with Albert-Laszlo Barabasi, of the Barabasi-Albert model of scale-free networks.
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What they believe
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Systems as different as a genome and the web are best described as networks with complex topology rather than as ordered or random structures.
Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology.
arxiv.orgSaid 21 Oct 1999This on korrents
The bold line is korrents’ wording; the quote is theirs. These one: what they wrote before what they said aloud, one per source, newest first.
Beliefs
Korrents What they believe 4 beliefs — each backed by an exact quote.
Each is a — compiled by korrents.com, not by them: the one-line wordings are korrents', the quotes are theirs.
Recent
Systems as different as a genome and the web are best described as networks with complex topology rather than as ordered or random structures.
Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
Large real-world networks share one property: the number of connections per node follows a scale-free power law.
A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
Scale-free structure follows from just two mechanisms: networks keep adding nodes, and new nodes attach preferentially to well-connected ones.
This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
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Because two generic mechanisms reproduce the observed distributions, large networks are governed by self-organizing rules that do not depend on what the network is made of.
A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
Beliefs others hold too
Systems as different as a genome and the web are best described as networks with complex topology rather than as ordered or random structures. 2 hold this
Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
Large real-world networks share one property: the number of connections per node follows a scale-free power law. 2 hold this
A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
Scale-free structure follows from just two mechanisms: networks keep adding nodes, and new nodes attach preferentially to well-connected ones. 2 hold this
This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites.
Barabasi & Albert, "Emergence of scaling in random networks" (arXiv) Said 21 Oct 1999
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