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dc.contributor.authorGamarnik, David
dc.date.accessioned2014-06-09T15:09:38Z
dc.date.available2014-06-09T15:09:38Z
dc.date.issued2013-09
dc.date.submitted2013-08
dc.identifier.issn0178-8051
dc.identifier.issn1432-2064
dc.identifier.urihttp://hdl.handle.net/1721.1/87698
dc.description.abstractThe paper is devoted to the problem of establishing right-convergence of sparse random graphs. This concerns the convergence of the logarithm of number of homomorphisms from graphs or hyper-graphs GN,N≥1 to some target graph W . The theory of dense graph convergence, including random dense graphs, is now well understood (Borgs et al. in Ann Math 176:151–219, 2012; Borgs et al. in Adv Math 219:1801–1851, 2008; Chatterjee and Varadhan in Eur J Comb 32:1000–1017, 2011; Lovász and Szegedy in J Comb Theory Ser B 96:933–957, 2006), but its counterpart for sparse random graphs presents some fundamental difficulties. Phrased in the statistical physics terminology, the issue is the existence of the limits of appropriately normalized log-partition functions, also known as free energy limits, for the Gibbs distribution associated with W . In this paper we prove that the sequence of sparse Erdös-Rényi graphs is right-converging when the tensor product associated with the target graph W satisfies a certain convexity property. We treat the case of discrete and continuous target graphs W . The latter case allows us to prove a special case of Talagrand’s recent conjecture [more accurately stated as level III Research Problem 6.7.2 in his recent book (Talagrand in Mean Field Models for Spin Glasses: Volume I: Basic examples. Springer, Berlin, 2010)], concerning the existence of the limit of the measure of a set obtained from RN by intersecting it with linearly in N many subsets, generated according to some common probability law. Our proof is based on the interpolation technique, introduced first by Guerra and Toninelli (Commun Math Phys 230:71–79, 2002) and developed further in (Abbe and Montanari in On the concentration of the number of solutions of random satisfiability formulas, 2013; Bayati et al. in Ann Probab Conference version in Proceedings of 42nd Ann. Symposium on the Theory of Computing (STOC), 2010; Contucci et al. in Antiferromagnetic Potts model on the Erdös-Rényi random graph, 2011; Franz and Leone in J Stat Phys 111(3/4):535–564, 2003; Franz et al. in J Phys A Math Gen 36:10967–10985, 2003; Montanari in IEEE Trans Inf Theory 51(9):3221–3246, 2005; Panchenko and Talagrand in Probab Theory Relat Fields 130:312–336, 2004). Specifically, Bayati et al. (Ann Probab Conference version in Proceedings of 42nd Ann. Symposium on the Theory of Computing (STOC), 2010) establishes the right-convergence property for Erdös-Rényi graphs for some special cases of W . In this paper most of the results in Bayati et al. (Ann Probab Conference version in Proceedings of 42nd Ann. Symposium on the Theory of Computing (STOC), 2010) follow as a special case of our main theorem.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF grant CMMI-1031332)en_US
dc.language.isoen_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00440-013-0528-6en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRight-convergence of sparse random graphsen_US
dc.typeArticleen_US
dc.identifier.citationGamarnik, David. “Right-Convergence of Sparse Random Graphs.” Probability Theory and Related Fields (September 14, 2013).en_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.mitauthorGamarnik, Daviden_US
dc.relation.journalProbability Theory and Related Fieldsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGamarnik, Daviden_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8898-8778
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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