Identifying High Centrality Nodes using Resampling-based Gap Analysis

Abstract

We address a problem of identifying high centrality nodes in a large social network based on approximated centrality values derived from a small portion of nodes sampled uniformly at random from the whole set. To this end, we apply our resampling-based framework to estimate the approximation error, and detect gaps between nodes with a given confidence level. Here, a gap means a clear difference between two nodes in terms of a centrality measure, and gap detection means, given two nodes, determining which node has a greater centrality value than the other with a given confidence level. On two real world social networks, we empirically show that the proposed method can successfully detect more gaps only from several tens of percent of the node, compared to the one adopting a standard error estimation framework, and that the resulting gaps enable us to correctly identify a set of nodes having a high centrality value.