Distribution and Dependence of Extremes in Network Sampling Processes

TL;DR

This paper analyzes the extremal dependence in network sampling processes, deriving and estimating the extremal index to compare sampling methods and study degree correlations in large networks.

Contribution

It introduces an analytical and empirical approach to compute the extremal index for network sampling, aiding comparison of sampling procedures and understanding degree correlations.

Findings

Derived the extremal index analytically for network sampling.

Estimated the extremal index empirically for different sampling methods.

Compared sampling procedures using the extremal index as a metric.

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.