Global Rank Estimation

TL;DR

This paper introduces fast, efficient methods for estimating a node's global centrality rank in large, dynamic networks without computing all centrality values, using sampling, structural analysis, or machine learning.

Contribution

It proposes novel techniques for estimating node centrality ranks efficiently, reducing the need for full network analysis in large-scale, real-world networks.

Findings

Methods effectively estimate degree and closeness centrality ranks

Sampling and machine learning approaches improve estimation speed

Applicable to large, dynamic networks with minimal accuracy loss

Abstract

In real world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application requirements. These centrality measures assign a centrality value to each node that denotes its importance index. But in real life applications, we are more interested in the relative importance of the node that can be measured using its centrality rank based on the given centrality measure. To compute the centrality rank of a node, we need to compute the centrality value of all the nodes and compare them to get the rank. This process requires the entire network. So, it is not feasible for real-life applications due to the large size and dynamic nature of real world networks. In the present project, we aim to propose fast and efficient methods to estimate the global centrality rank of a node without computing the centrality value of all nodes. These methods can be further extended to estimate the rank without having the entire network. The proposed methods use the structural behavior of centrality measures, sampling techniques, or the machine learning models. In this work, we also discuss how to apply these methods for degree and closeness centrality rank estimation.