Ranking the spreading influence in complex networks

TL;DR

This paper proposes an improved, parameterless method for ranking node influence in networks by considering shortest distances to high $k$-core nodes, outperforming traditional centrality measures in accuracy.

Contribution

The paper introduces a novel influence ranking method based on shortest distances to high $k$-core nodes, enhancing accuracy over existing measures.

Findings

The new method outperforms degree, closeness, $k$-shell, and mixed degree methods.

It accurately identifies influential nodes in real and synthetic networks.

The approach is parameterless and computationally efficient.

Abstract

Identifying the node spreading influence in networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the shortest distance between a target node and the node set with the highest $k$-core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barab\'{a}si-Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree $k$, closeness centrality, $k$-shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.