Sign inSubmit
ArticleMetrics
Research Article · Open access · Published 29 November 2017

Local Large Deviations: A McMillian Theorem for Typed Random Graph Processes

Kwabena Doku-Amponsah1

Journal of Mathematics and Statistics, Volume 13, Issue 4, pp. 347–352 · doi.org/10.3844/jmssp.2017.347.352

7,114 accesses3,666 downloads3 citations (Crossref)Metrics
Cite Check for updates

Abstract

For a finite typed graph on n nodes and with type law µ on finite alphabet Γ, we define the spectral potential ρλ(., µ), of the graph. From the ρλ(., µ) we define the Kullback action or the divergence function, Hλ(π || v), with respect to an empirical link measure, π, as the Legendre dual. For the finite typed random graph conditioned to have an empirical link measure π and empirical type measure µ, we prove a Local large Deviation Principle (LLDP), with rate function Hλ(π || v) and speed n. From this LLDP, we obtain a full conditional large deviation principle and a weak variant of the classical McMillian Theorem for the typed random graph models. Given the typical empirical link measure, λµ⊗µ, we show that, the number of typed random graphs is equal to en||λµ⊗µ||H(λµ⊗µ⁄||λµ⊗µ||) approximately. Note that we do not require any topological restrictions on the space of finite graphs for these LLDPs.

Licence and reuse

Open access. This article is licensed under a Creative Commons Attribution 3.0 Licence (CC BY 3.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

How to reuse this article

Responses (0)

Have a comment on this research? Sign in to respond. Responses are moderated and published with your name and affiliation; the authors are invited to reply. How we use your dataWrite a response

About this article

Cite this article

Doku-Amponsah, K. (2017). Local Large Deviations: A McMillian Theorem for Typed Random Graph Processes. Journal of Mathematics and Statistics, 13(4), 347–352. https://doi.org/10.3844/jmssp.2017.347.352

Licence
CC BY 3.0
Issue
Volume 13, Issue 4
Policies
Editorial independenceUse of generative AIData availabilityAppeals and complaints
Version
Version of record
Local Large Deviations: A McMillian Theorem for Typed Random Graph ProcessesJournal of Mathematics and Statistics · Volume 13, Issue 4 · pp. 347–352 · CC BY 3.0Download