Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling

Résumé

Statistical models for networks have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are explicitly designed for capturing some specific graph properties (such as power-law degree distributions), which makes them unsuitable for application to domains where the behavior of the target quantities is not known a priori. The key contribution of this paper is twofold. First, we introduce the Fiedler delta statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random field model, which allows for efficient estimation of edge distributions over large-scale random networks. After analyzing the dependence structure involved in Fiedler random fields, we estimate them over several real-world networks, showing that they achieve a much higher modeling accuracy than other well-known statistical approaches.
Fichier principal
Vignette du fichier
extended_paper.pdf (119.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00750345 , version 1 (22-11-2012)

Identifiants

  • HAL Id : hal-00750345 , version 1

Citer

Antonino Freno, Mikaela Keller, Marc Tommasi. Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling. Neural Information Processing Systems (NIPS), Dec 2012, Lake Tahoe, United States. ⟨hal-00750345⟩
213 Consultations
119 Téléchargements

Partager

Gmail Facebook X LinkedIn More