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Predicting the structural evolution of networks by applying multivariate time series

Author

Listed:
  • Huang, Qiangjuan
  • Zhao, Chengli
  • Wang, Xiaojie
  • Zhang, Xue
  • Yi, Dongyun

Abstract

In practice, complex systems often change over time, and the temporal characteristics of a complex network make their behavior difficult to predict. Traditional link prediction methods based on structural similarity are good for mining underlying information from static networks, but do not always capture the temporal relevance of dynamic networks. However, time series analysis is an effective tool for examining dynamic evolution. In this paper, we combine link prediction with multivariate time series analysis to describe the structural evolution of dynamic networks using both temporal information and structure information. An empirical analysis demonstrates the effectiveness of our method in predicting undiscovered linkages in two classic networks.

Suggested Citation

  • Huang, Qiangjuan & Zhao, Chengli & Wang, Xiaojie & Zhang, Xue & Yi, Dongyun, 2015. "Predicting the structural evolution of networks by applying multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 470-480.
    • Handle: RePEc:eee:phsmap:v:428:y:2015:i:c:p:470-480
    DOI: 10.1016/j.physa.2015.02.019
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    References listed on IDEAS

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    1. Xie, Zheng & Dong, Enming & Li, Jianping & Kong, Dexing & Wu, Ning, 2014. "Potential links by neighbor communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 244-252.
    2. Zhang, Xue & Wang, Xiaojie & Zhao, Chengli & Yi, Dongyun & Xie, Zheng, 2014. "Degree-corrected stochastic block models and reliability in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 553-559.
    3. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    4. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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    Citations

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    Cited by:

    1. Yu, Xuan & Shi, Suixiang & Xu, Lingyu & Yu, Jie & Liu, Yaya, 2020. "Analyzing dynamic association of multivariate time series based on method of directed limited penetrable visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Huang, Qiangjuan & Zhao, Chengli & Zhang, Xue & Yi, Dongyun, 2017. "Locating the source of spreading in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 434-444.
    3. Liu, Yangyang & Zhao, Chengli & Wang, Xiaojie & Huang, Qiangjuan & Zhang, Xue & Yi, Dongyun, 2016. "The degree-related clustering coefficient and its application to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 24-33.
    4. Castro, Luis E. & Shaikh, Nazrul I., 2018. "A particle-learning-based approach to estimate the influence matrix of online social networks," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 1-18.
    5. Petre Caraiani, 2020. "Forecasting Financial Networks," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 983-997, March.

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