Identifying Influential Spreaders Using Local Information

Zhe Li () and Xinyu Huang ()
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Zhe Li: Software College, Shenyang University of Technology of China, Shenyang 110870, China
Xinyu Huang: Software College, Northeastern University of China, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 6, 1-14

Abstract: The heterogeneous nature indicates that different nodes may play different roles in network structure and function. Identifying influential spreaders is crucial for understanding and controlling the spread processes of epidemic, information, innovations, and so on. So how to identify influential spreaders is an urgent and crucial issue of network science. In this paper, we propose a novel local-information-based method, which can obtain the degree information of nodes’ higher-order neighbors by only considering the directly connected neighbors. Specifically, only a few iterations are needed to be executed, the degree information of nodes’ higher-order neighbors can be obtained. In particular, our method has very low computational complexity, which is very close to the degree centrality, and our method is of great extensibility, with which more factors can be taken into account through proper modification. In comparison with the well-known state-of-the-art methods, experimental analyses of the Susceptible-Infected-Recovered (SIR) propagation dynamics on ten real-world networks evidence that our method generally performs very competitively.

Keywords: influential spreaders; local information; complex network; network information mining (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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