Distribution and Dependence of Extremes in Network Sampling Processes

Abstract : We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study extremal properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers, or income of the nodes in online social networks, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like the kth largest value, clusters of exceedances over a threshold, and first hitting time of a large value are investigated. We abstract the dependence and the statistics of extremes into a single parameter that appears in extreme value theory called extremal index (EI). In this work, we derive this parameter analytically and also estimate it empirically. We propose the use of EI as a parameter to compare different sampling procedures. As a specific example, degree correlations between neighboring nodes are studied in detail with three prominent random walks as sampling techniques.
Type de document :
Article dans une revue
Computational Social Networks, Springer, 2015, 2 (12), pp.1-21. 〈http://www.computationalsocialnetworks.com/〉. 〈10.1186/s40649-015-0018-3〉
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

Contributeur : Konstantin Avrachenkov <>
Soumis le : mardi 19 janvier 2016 - 17:21:15
Dernière modification le : samedi 27 janvier 2018 - 01:31:42
Document(s) archivé(s) le : vendredi 11 novembre 2016 - 13:21:45


Fichiers éditeurs autorisés sur une archive ouverte




Konstantin Avrachenkov, Natalia M. Markovich, Jithin K. Sreedharan. Distribution and Dependence of Extremes in Network Sampling Processes. Computational Social Networks, Springer, 2015, 2 (12), pp.1-21. 〈http://www.computationalsocialnetworks.com/〉. 〈10.1186/s40649-015-0018-3〉. 〈hal-01259001〉



Consultations de la notice


Téléchargements de fichiers