Iterative resource allocation based on propagation feature of node for identifying the influential nodes

TL;DR

This paper introduces an improved iterative resource allocation method that leverages node propagation features and spreading rates to more accurately identify influential nodes in networks, outperforming traditional methods.

Contribution

The paper proposes a novel IIRA method that incorporates neighbor centrality and spreading influence, enhancing influential node detection accuracy over existing approaches.

Findings

IIRA outperforms traditional IRA in identifying influential nodes.

Kendall's tau improved by 23% in Erdos network at spreading rate 0.12.

Kendall's tau improved by 24% in Protein network at spreading rate 0.08.

Abstract

The Identification of the influential nodes in networks is one of the most promising domains. In this paper, we present an improved iterative resource allocation (IIRA) method by considering the centrality information of neighbors and the influence of spreading rate for a target node. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the IIRA method could identify influential nodes more accurately than the tradition IRA method. Specially, in the Erdos network, the Kendall's tau could be enhanced 23\% when the spreading rate is 0.12. In the Protein network, the Kendall's tau could be enhanced 24\% when the spreading rate is 0.08.