Conférence
Notice
Lieu de réalisation
Paris
Langue :
Anglais
Crédits
INRIA (Institut national de recherche en informatique et automatique) (Production), INRIA (Institut national de recherche en informatique et automatique) (Publication), François Baccelli (Publication), Venkat Anantharam (Intervention)
Conditions d'utilisation
Droit commun de la propriété intellectuelle
DOI : 10.60527/qx7y-5r15
Citer cette ressource :
Venkat Anantharam. Inria. (2019, 20 mars). A notion of entropy for limits of sparse marked graphs (workshop ERC Nemo Processus ponctuels et graphes aléatoires unimodulaires) , in Workshop Processus ponctuels et graphes aléatoires unimodulaires [ERC Nemo] (20-22 mars 2019). [Vidéo]. Canal-U. https://doi.org/10.60527/qx7y-5r15. (Consultée le 12 mai 2024)

A notion of entropy for limits of sparse marked graphs (workshop ERC Nemo Processus ponctuels et graphes aléatoires unimodulaires)

Réalisation : 20 mars 2019 - Mise en ligne : 21 mai 2019
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Descriptif

Bordenave and Caputo (2014) defined a notion of entropy for probability distributions on rooted graphs with finite expected degree at the root. When such a probability distribution \rho has finite BC entropy \Sigma(\rho), the growth in the number of vertices n of the number of graphs on n vertices whose associated rooted graph distribution is close to \rho is as d/2 n \log n + \Sigma(\rho) n + o(n), where d is expected degree of the root under \rho. We develop the parallel result for probability distributions on marked rooted graphs. Our graphs have vertex marks drawn from a finite set and directed edge marks, one towards each vertex, drawn from a finite set. The talk will focus on presenting an overview of the technical details of this extension We are motivated by the interpretation of a discrete time stochastic process taking values in a finite set \Theta as the local weak limit of long strings of symbols from \Theta. We argue that probability distributions on marked rooted graphs are the natural analogs of stochastic process models for *graphical data*, by which we mean data indexed by the vertices and edges of a sparse graph rather than by linearly ordered time. Our extension of the BC entropy can then be argued to be the natural extension, in the world of graphical data, of the Shannon entropy rate in the world of time series. We illustrate this viewpoint by proving a lossless data compression theorem analogous to the basic lossless data compression theorem for time series. Joint work with Payam Delgosha.

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