A novel method for identifying influential nodes in complex networks based on gravity model

Jiang, Yuan; Yang, Song-Qing; Yan, Yuwei; Tong, Tian-Chi; Dai, Jiyang

doi:10.1088/1674-1056/ac4226

Cited by 5 publications

(2 citation statements)

References 31 publications

(48 reference statements)

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“…This method simultaneously utilizes seven metrics as the features of structural hole nodes to comprehensively rank nodes in the network. Jiang et al [18] introduced an innovative method termed EGMS, which thoroughly analyzes the structural hole features and K-shell centrality of nodes, taking into account both local topology and global location information. These methods have not considered the influence of network communities on node centrality.…”

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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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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“…Analyzing the various properties of complex networks will facilitate our understanding of the characteristics of individual networks and, thus, the structure and functions of the networks and the connections between them. It will also provide a theoretical basis for exploiting and controlling the networks [1] . In a network, the individual entities are represented by nodes, and the edges represent the connections between the entities.…”

Section: Introductionmentioning

confidence: 99%

Key node recognition based on mixed degree decomposition method

Wu,

chen,

Yue

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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Key node recognition is an essential component in the field of complex networks. To address the problems of low resolution and high computational cost of key node recognition methods in complex networks, an improved mixed degree decomposition method (IMDD) is proposed in this paper to identify key nodes. Firstly, The mixed degree decomposition method is used to stratify the network and determine the mixed K-shell (Km) value of each node to globally quantify the importance of the nodes. Secondly, the concept of the influence coefficient of sub-neighbors is proposed, and the dynamically adjustable influence coefficient of sub-neighbors of each node is calculated. Finally, a formula for the comprehensive score of node importance is constructed, and the comprehensive score of each node is calculated by balancing the influence of neighbors and sub-neighbors and thus ranking the importance of nodes in the network. The parameters in the Km calculation formula are pre-tested in six realistic networks of different sizes and compared with the other six classical methods through SIR contagion model simulation experiments. The experimental results show that changes in parameter values have minimal impact on the identification of key nodes, and the method can effectively identify key nodes, outperforming the other six classical methods in terms of resolution, accuracy, and relevance, and maintaining a lower The results show that the method can effectively identify key nodes and outperforms the other six classical methods in terms of resolution, accuracy and relevance, and maintains a low time complexity.

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RETRACTED ARTICLE: Identifying vital nodes in hypernetwork based on local centrality

Wei

et al. 2022

J Comb Optim

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A novel method for identifying influential nodes in complex networks based on gravity model

Cited by 5 publications

References 31 publications

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

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

Key node recognition based on mixed degree decomposition method

RETRACTED ARTICLE: Identifying vital nodes in hypernetwork based on local centrality

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