Skip to Main content Skip to Navigation

Distribution and Dependence of Extremes in Network Sampling Processes

Abstract : We explore the dependence structure in the sampled sequence of large 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 etc, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like $k$th largest value, clusters of exceedances over a threshold, first hitting time of a large value etc 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.
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download
Contributor : Jithin Sreedharan Connect in order to contact the contributor
Submitted on : Tuesday, February 24, 2015 - 10:14:05 AM
Last modification on : Wednesday, February 2, 2022 - 3:57:30 PM
Long-term archiving on: : Monday, May 25, 2015 - 10:26:06 AM


Files produced by the author(s)


  • HAL Id : hal-01054929, version 3
  • ARXIV : 1408.2529



Konstantin Avrachenkov, Natalia M. Markovich, Jithin K. Sreedharan. Distribution and Dependence of Extremes in Network Sampling Processes. [Research Report] RR-8578, Inria. 2014, pp.25. ⟨hal-01054929v3⟩



Record views


Files downloads