Identify influential spreaders in complex networks, the role of neighborhood

TL;DR

This paper introduces a neighborhood centrality measure that combines a node's centrality with that of its neighbors, significantly improving the identification of influential spreaders in complex networks.

Contribution

It proposes a novel neighborhood centrality measure that outperforms traditional centrality metrics in ranking influential nodes in real-world networks.

Findings

Neighborhood centrality outperforms degree and coreness in influence ranking.

Considering 2-step neighborhoods balances performance and computational cost.

Adding more steps beyond 2-step does not improve and may reduce accuracy.

Abstract

Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors' centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.