Identifying influential spreaders in complex networks based on density entropy and community structure

doi:10.1088/1674-1056/ad20d6

Abstract In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible-infected-recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

Keywords: complex networks influential spreaders propagation model static attack

Received: 17 October 2023
Revised: 17 January 2024
Accepted manuscript online: 22 January 2024

PACS:

89.75.Fb

(Structures and organization in complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61803264).

Corresponding Authors: Lei Chen
E-mail: 213330655@st.usst.edu.cn

Cite this article:

Zhan Su(苏湛), Lei Chen(陈磊), Jun Ai(艾均), Yu-Yu Zheng(郑雨语), and Na Bie(别娜) Identifying influential spreaders in complex networks based on density entropy and community structure 2024 Chin. Phys. B 33 058901

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