Indexing metadata

High-Dimensional Random Geometric Graphs and their Clique Number


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document High-Dimensional Random Geometric Graphs and their Clique Number
 
2. Creator Author's name, affiliation, country Luc Devroye; McGill University; Canada
 
2. Creator Author's name, affiliation, country András György; Hungarian Academy of Sciences; Hungary
 
2. Creator Author's name, affiliation, country Gábor Lugosi; Pompeu Fabra University; Spain
 
2. Creator Author's name, affiliation, country Frederic Udina; Pompeu Fabra University; Spain
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Clique number; dependency testing; geometric graphs; random graphs
 
3. Subject Subject classification 05C80; 62H15
 
4. Description Abstract We study the behavior of random geometric graphs in high dimensions. We show that as the dimension grows, the graph becomes similar to an Erdös-Rényi random graph. We pay particular attention to the clique number of such graphs and show that it is very close to that of the corresponding Erdös-Rényi graph when the dimension is larger than $\log^3(n)$ where $n$ is the number of vertices. The problem is motivated by a statistical problem of testing dependencies.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) Partially supported by the National Development Agency of Hungary, from the Research and Technological Innovation Fund (KTIA-OTKA CNK 77782) Spanish Ministry of Science and Technology grant MTM2009-09063 and by the PASCAL Network of Excellence under EC gr
 
7. Date (YYYY-MM-DD) 2011-11-30
 
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/967
 
10. Identifier Digital Object Identifier 10.1214/EJP.v16-967
 
11. Source Journal/conference title; vol., no. (year) Electronic Journal of Probability; Vol 16
 
12. Language English=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.