Identifying Influential Spreaders by Weighted LeaderRank

TL;DR

This paper introduces a weighted version of the LeaderRank algorithm that improves the identification of influential spreaders in social networks by enhancing accuracy, noise tolerance, and robustness against attacks.

Contribution

The paper proposes a novel weighted LeaderRank algorithm that outperforms the original in identifying influential spreaders and resisting data noise and attacks.

Findings

Weighted LeaderRank finds more influential spreaders

It has higher tolerance to noisy data

It is more robust to intentional attacks

Abstract

Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank [L. Lv et al., PLoS ONE 6 (2011) e21202]. According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders, (ii) the higher tolerance to noisy data, and (iii) the higher robustness to intentional attacks.