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Information Centrality Evaluation Method Based on Cascade Topological Relevance

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1491))

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Abstract

Unmanned systems can be abstracted as dynamic and complex systems of multi-agent competition and cooperation. Its quantitative and qualitative characteristics are naturally similar to those in network science. Therefore, we can explore how to form a dynamic and efficient adjustment of the link relationship between nodes, based on studying of structural complexity, node complexity, and interactions between structure and nodes in network science. The aforementioned outputs can accordingly support the efficiency of information interaction and dissemination between nodes. To solve the problem of information cooperation in weak communication connection of unmanned systems, this paper proposed an information centrality evaluation method based on the degree of cascaded topology correlation (CTRICE, Cascade Topological Relevance Information Centrality Evaluation). The evaluation method and strategy of cascading topology association degree based on local neighborhood were formed through the evaluation of cascading information aggregation ability within the neighborhood and the evaluation of intimacy based on topology and interaction behavior. Consequently, the results of the assessment would provide support for information fusion and decision-making. This paper first proves the feasibility of this method in terms of information synergy consistency. Meanwhile, it compares and analyzes the convergence efficiency through simulation experiments between the proposed method with assessment methods of mean value and degree centrality. Compared with the traditional method, the proposed method has better robustness and robustness under the condition of low quality communication connections. The method presented in this paper provides an idea for the realization of information self-organization and collaboration based on topological relations in unmanned systems.

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Author information

Authors and Affiliations

  1. National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China

    Yuting Shen

  2. University of Chinese Academy of Sciences, Beijing, 100039, People’s Republic of China

    Yuting Shen

  3. The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050000, People’s Republic of China

    Yuting Shen, Yueqing Gao, Lulu Chen & Chu Du

  4. China Academy of Launch Vehicle Technology, Beijing, 100076, People’s Republic of China

    Kaixuan Wang

  5. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, People’s Republic of China

    Kaixuan Wang

  6. Beijing Jiaotong University, Beijing, China

    Yueqing Gao

  7. Center for Future Multimedia and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610051, China

    Lulu Chen

Authors
  1. Yuting Shen
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  2. Kaixuan Wang
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  3. Yueqing Gao
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  4. Lulu Chen
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  5. Chu Du
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Corresponding author

Correspondence to Yuting Shen .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Hunan University of Science and Technology, Xiangtan, China

    Buqing Cao

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. University of Warwick, Coventry, UK

    Bowen Du

  7. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

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Shen, Y., Wang, K., Gao, Y., Chen, L., Du, C. (2022). Information Centrality Evaluation Method Based on Cascade Topological Relevance. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_19

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  • DOI: https://doi.org/10.1007/978-981-19-4546-5_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4545-8

  • Online ISBN: 978-981-19-4546-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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