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Réka Albert

Réka Albert

@reka-albert · 4 positions · On ppll ↗

Professor of physics and biology at Penn State, and co-author of the model that explains why large networks are scale-free.

Réka Albert did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

3 of Réka Albert's claims fit 2 mental models. Most often: Emergence, Power laws.

Models they name

Their own words name the idea.

Power laws

In many things a few cases account for most of the result, so the average misleads and the outlier decides.

Used by 25 others

Large real-world networks share one property: the number of connections per node follows a scale-free power law.

  1. Réka Albert Professor of physics and biology at Penn State 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)arxiv.org · 21 Oct 1999All korrents from this piece
    Their wordsA 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)arxiv.org

    21 Oct 1999

Models we see in what they say

Our reading: their claim applies the idea without naming it. The claim is theirs; filing it here is ours.

Emergence

Whole systems do things none of their parts do, and nobody has to design that order for it to appear.

Used by 24 others

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.

  1. Réka Albert Professor of physics and biology at Penn State 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)arxiv.org · 21 Oct 1999All korrents from this piece
    Their wordsA 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)arxiv.org

    21 Oct 1999

Scale-free structure follows from just two mechanisms: networks keep adding nodes, and new nodes attach preferentially to well-connected ones.

  1. Réka Albert Professor of physics and biology at Penn State 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)arxiv.org · 21 Oct 1999All korrents from this piece
    Their wordsThis 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)arxiv.org

    21 Oct 1999