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Identifying influential spreaders based on diffusion K-truss decomposition.

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Title: Identifying influential spreaders based on diffusion K-truss decomposition.
Authors: Yang, Li1 yangli_njupt_edu@126.com, Song, Yu-Rong1 songyr@njupt.edu.cn, Jiang, Guo-Ping1 jianggp@njupt.edu.cn, Xia, Ling-Ling2 xialingling@gjspi.edu.cn
Source: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 9/10/2018, Vol. 32 Issue 22, pN.PAG-N.PAG. 12p. 2 Diagrams, 3 Charts, 2 Graphs.
Subjects: Subgraphs, Mathematical decomposition, Graph theory, Monte Carlo method, Computer simulation
Abstract: Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 131313945
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  Data: <searchLink fieldCode="DE" term="%22Subgraphs%22">Subgraphs</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+decomposition%22">Mathematical decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink>
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  Data: Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0217979218502387
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      – Code: eng
        Text: English
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      – SubjectFull: Subgraphs
        Type: general
      – SubjectFull: Mathematical decomposition
        Type: general
      – SubjectFull: Graph theory
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      – SubjectFull: Monte Carlo method
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      – SubjectFull: Computer simulation
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      – TitleFull: Identifying influential spreaders based on diffusion K-truss decomposition.
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            NameFull: Song, Yu-Rong
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            NameFull: Jiang, Guo-Ping
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              M: 09
              Text: 9/10/2018
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              Y: 2018
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            – TitleFull: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics
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