Indexing metadata

On the non-Gaussian fluctuations of the giant cluster for percolation on random recursive trees


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document On the non-Gaussian fluctuations of the giant cluster for percolation on random recursive trees
 
2. Creator Author's name, affiliation, country Jean Bertoin; Universität Zürich; Switzerland
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Random recursive tree, giant cluster, fluctuations, super-critical percolation.
 
3. Subject Subject classification 60F05; 05C05
 
4. Description Abstract We consider a Bernoulli bond percolation on a random recursive tree of size $n\gg 1$, with supercritical parameter $p_n=1-c/\ln n$ for some $c>0$ fixed. It is known that with high probability, there exists then a unique giant cluster of size $G_n\sim e^{-c}n$, and it follows from a recent result of Schweinsberg that $G_n$ has non-Gaussian fluctuations. We provide an  explanation of this  by analyzing the effect of percolation on different phases of the growth of recursive trees.  This alternative approach may be useful for studying percolation on other classes of trees, such as for instance regular trees.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2014-02-27
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://ejp.ejpecp.org/article/view/2822
 
10. Identifier Digital Object Identifier 10.1214/EJP.v19-2822
 
11. Source Journal/conference title; vol., no. (year) Electronic Journal of Probability; Vol 19
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions The Electronic Journal of Probability applies the Creative Commons Attribution License (CCAL) to all articles we publish in this journal. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles published in EJP, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.

Summary of the Creative Commons Attribution License

You are free
  • to copy, distribute, display, and perform the work
  • to make derivative works
  • to make commercial use of the work
under the following condition of Attribution: others must attribute the work if displayed on the web or stored in any electronic archive by making a link back to the website of EJP via its Digital Object Identifier (DOI), or if published in other media by acknowledging prior publication in this Journal with a precise citation including the DOI. For any further reuse or distribution, the same terms apply. Any of these conditions can be waived by permission of the Corresponding Author.