The statistical nature of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>h</mml:mi></mml:math>-index of a network node and its extensions

Liu, Yan; Mudi, Jiang,; Hu, Lianyu; He, Zengyou

doi:10.1016/j.joi.2023.101424

Cited by 4 publications

(1 citation statement)

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“…Complex networks have many classic centrality algorithms, such as degree centrality, [4] H-index centrality, [5] betweenness centrality, [6] closeness centrality, [7] eigenvector centrality, [8] PageRank, [9] and K-shell. [10] These methods measure the importance of nodes from different perspectives, including node neighbors, global paths, eigenvectors, and network positions.…”

Section: Introductionmentioning

confidence: 99%

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

Su 苏,

Chen 陈,

Ai 艾

et al. 2024

Chinese Phys. B

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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 therefore 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.

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