Path diversity improves the identification of influential spreaders

TL;DR

This paper introduces the concept of path diversity to improve the identification of influential spreaders in complex networks, demonstrating that considering path diversity enhances ranking accuracy and efficiency over existing methods.

Contribution

It proposes a novel local method that combines path number and path diversity, outperforming existing techniques in large-scale network analysis.

Findings

Path diversity significantly improves ranking accuracy.

The proposed method outperforms well-known existing methods.

The method is efficient and scalable to large networks.

Abstract

Identifying influential spreaders in complex networks is a crucial problem which relates to wide applications. Many methods based on the global information such as $k$-shell and PageRank have been applied to rank spreaders. However, most of related previous works overwhelmingly focus on the number of paths for propagation, while whether the paths are diverse enough is usually overlooked. Generally, the spreading ability of a node might not be strong if its propagation depends on one or two paths while the other paths are dead ends. In this Letter, we introduced the concept of path diversity and find that it can largely improve the ranking accuracy. We further propose a local method combining the information of path number and path diversity to identify influential nodes in complex networks. This method is shown to outperform many well-known methods in both undirected and directed networks. Moreover, the efficiency of our method makes it possible to be applied to very large systems.