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Identifying hidden target nodes for spreading in complex networks

Author

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  • Yin, Haofei
  • Zhang, Aobo
  • Zeng, An

Abstract

Using measurable data to realize targeted spreading of vital nodes in complex networks is an important issue connecting to various real applications such as commercial advertising, medication selection, and even military attack. However, a significant challenge is that the target nodes are not always known, which hinders the best allocation of initial spreaders to maximize the affected target nodes. To address this issue, this study develops a general framework to map the target node identification problem to the solution of underdetermined equations. Similar to the sparse signal reconstruction problem, it can be solved by the standard compressed sensing algorithm. Our research is completely driven by the limited data fed back after each spread realization. The experimental results show that this decoding method can efficiently achieve a high calculation accuracy both in the artificial networks and the actual networks. Finally, the effects of network structure, infection probability and initial spreader on the accuracy are discussed, aiming to provide theoretical guidance and new enlightenment for practical applications.

Suggested Citation

  • Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000048
    DOI: 10.1016/j.chaos.2023.113103
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    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Franz Kaiser & Vito Latora & Dirk Witthaut, 2021. "Network isolators inhibit failure spreading in complex networks," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    4. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2019. "Interacting model of rumor propagation and behavior spreading in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 168-177.
    5. Zhesi Shen & Wen-Xu Wang & Ying Fan & Zengru Di & Ying-Cheng Lai, 2014. "Reconstructing propagation networks with natural diversity and identifying hidden sources," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
    6. Flaviano Morone & Hernán A. Makse, 2015. "Correction: Corrigendum: Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 527(7579), pages 544-544, November.
    7. Qu, Junyi & Tang, Ming & Liu, Ying & Guan, Shuguang, 2020. "Identifying influential spreaders in reversible process," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Zhao, Laijun & Qiu, Xiaoyan & Wang, Xiaoli & Wang, Jiajia, 2013. "Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 987-994.
    9. Zan, Yongli, 2018. "DSIR double-rumors spreading model in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 191-202.
    10. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    11. Patricia M. Gregg & Jian Lin & Mark D. Behn & Laurent G. J. Montési, 2007. "Spreading rate dependence of gravity anomalies along oceanic transform faults," Nature, Nature, vol. 448(7150), pages 183-187, July.
    12. Yang, Anzhi & Huang, Xianying & Cai, Xiumei & Zhu, Xiaofei & Lu, Ling, 2019. "ILSR rumor spreading model with degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    13. Weiping Wang & Saini Yang & H. Eugene Stanley & Jianxi Gao, 2019. "Local floods induce large-scale abrupt failures of road networks," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    14. Kuikka, Vesa & Monsivais, Daniel & Kaski, Kimmo K., 2022. "Influence spreading model in analysing ego-centric social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    15. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    16. Jianxi Gao & Yang-Yu Liu & Raissa M. D'Souza & Albert-László Barabási, 2014. "Target control of complex networks," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
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