Estimating Shell-Index in a Graph with Local Information

TL;DR

This paper introduces a local-information-based method to estimate the shell-index of nodes in large networks, enabling efficient identification of influential nodes without requiring global network data.

Contribution

It presents a novel local estimation technique for shell-index and a hill-climbing approach to quickly find top-ranked influential nodes.

Findings

Effective local estimation of shell-index demonstrated

Hill-climbing approach efficiently identifies top nodes

Method reduces need for global network information

Abstract

For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. The k-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. The k-shell method requires the global information of the network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this work, we propose a method to estimate the shell-index of a node using its local information. We also propose hill-climbing based approach to hit the top-ranked nodes in a small number of steps. We further discuss a method to estimate the rank of a node based on the proposed estimator.