Exploring influential nodes using global and local information

Hu, Haifeng; Sun, Zejun; Wang, Feifei; Zhang, Liwen; Wang, Guan

doi:10.1038/s41598-022-26984-4

Cited by 13 publications

(6 citation statements)

References 52 publications

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“… SIR infectious disease model 37 . The SIR model is a mathematical model applied to information transmission research, and an essential standard for evaluating important nodes in complex networks 38 . The SIR model splits the population into susceptible, infective, and removed categories, with the respective numbers of all populations at time t denoted by S(t), I(t), and R(t) 39 .…”

Section: Resultsmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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Section: Resultsmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

Self Cite

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“…It was suggested to leverage the information both locally and globally using the escape velocity measure of centrality and an extended escape velocity measure of centrality [3], which are based on the principle of escape velocity. Hu et al created a measure for determining crucial nodes called global and local information [30]. For determining vital nodes, a measure termed hybrid characteristic centrality and its extended version were presented [31].…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Gravity Model for Determining Crucial Nodes in Social Networks Based on Degree K-Shell Eigenvector Index

Singh,

Singh

2024

IEEE Access

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In the realm of network science, determining crucial nodes within a social network is an ongoing concern. As a result, it garners a lot of attention, and various centrality measures for the identification of crucial nodes have been proposed thus far. Degree and k-shell decomposition are the classic centrality measures that rely on neighboring nodes. However, degree, k-shell, and combination of degree and k-shell measures assign the identical value to the vast count of nodes, which creates a problem in distinguishing these nodes. Therefore, in this paper, for the purpose of solving the above problem, we propose an index based on three different components: degree, improved k-shell measure, and eigenvector centrality called the degree k-shell eigenvector (DKE) index. In addition, we propose an enhanced gravity model called the DKE-based gravity model (DKEGM) on the basis of universal gravity law and the proposed index for determining crucial nodes in social networks. The proposed gravity model incorporates different aspects of nodes, which include count of neighbors, location of nodes, influence of neighbors, and path information between the nodes. Numerous experiments are executed on eight real networks using the SIR model, Kendall tau, ranking monotonicity, and distinct metric to examine the effectiveness of the DKEGM with respect to the other measures. The empirical outcomes show the effectiveness of the DKEGM in terms of accuracy, distinguishing ability, and efficiency.INDEX TERMS Centrality measures, crucial nodes, degree k-shell eigenvector index, gravity model, social networks.

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“…(1) SIR infectious disease model 19 The SIR model is a mathematical model applied to information transmission research, and an essential standard for evaluating important nodes in complex networks 20 . The SIR model splits the population into susceptible, infective, and removed categories, with the respective numbers of all populations at time t denoted by S(t), I(t), and R(t) 21 .…”

Section: Evaluation Indexmentioning

confidence: 99%

Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

Zheng

Wang

et al. 2023

Preprint

Self Cite

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the SIR model as the evaluation criteria. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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Exploring influential nodes using global and local information

Cited by 13 publications

References 52 publications

Excavating important nodes in complex networks based on the heat conduction model

Excavating important nodes in complex networks based on the heat conduction model

An Enhanced Gravity Model for Determining Crucial Nodes in Social Networks Based on Degree K-Shell Eigenvector Index

Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

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