{ "id": "1005.1136", "version": "v5", "published": "2010-05-07T06:05:00.000Z", "updated": "2011-08-30T06:04:06.000Z", "title": "Random graphs with a given degree sequence", "authors": [ "Sourav Chatterjee", "Persi Diaconis", "Allan Sly" ], "comment": "Published in at http://dx.doi.org/10.1214/10-AAP728 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)", "journal": "Annals of Applied Probability 2011, Vol. 21, No. 4, 1400-1435", "doi": "10.1214/10-AAP728", "categories": [ "math.PR", "math.CO", "math.ST", "stat.TH" ], "abstract": "Large graphs are sometimes studied through their degree sequences (power law or regular graphs). We study graphs that are uniformly chosen with a given degree sequence. Under mild conditions, it is shown that sequences of such graphs have graph limits in the sense of Lov\\'{a}sz and Szegedy with identifiable limits. This allows simple determination of other features such as the number of triangles. The argument proceeds by studying a natural exponential model having the degree sequence as a sufficient statistic. The maximum likelihood estimate (MLE) of the parameters is shown to be unique and consistent with high probability. Thus $n$ parameters can be consistently estimated based on a sample of size one. A fast, provably convergent, algorithm for the MLE is derived. These ingredients combine to prove the graph limit theorem. Along the way, a continuous version of the Erd\\H{o}s--Gallai characterization of degree sequences is derived.", "revisions": [ { "version": "v5", "updated": "2011-08-30T06:04:06.000Z" } ], "analyses": { "keywords": [ "degree sequence", "random graphs", "natural exponential model", "maximum likelihood estimate", "graph limit theorem" ], "tags": [ "journal article" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2010arXiv1005.1136C" } } }