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Dynamic structure evolution of time-dependent network

Author

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  • Zhang, Beibei
  • Zhou, Yadong
  • Xu, Xiaoyan
  • Wang, Dai
  • Guan, Xiaohong

Abstract

In this paper, we research the long-voided problem of formulating the time-dependent network structure evolution scheme, it focus not only on finding new emerging vertices in evolving communities and new emerging communities over the specified time range but also formulating the complex network structure evolution schematic. Previous approaches basically applied to community detection on time static networks and thus failed to consider the potentially crucial and useful information latently embedded in the dynamic structure evolution process of time-dependent network. To address these problems and to tackle the network non-scalability dilemma, we propose the dynamic hierarchical method for detecting and revealing structure evolution schematic of the time-dependent network. In practice and specificity, we propose an explicit hierarchical network evolution uncovering algorithm framework originated from and widely expanded from time-dependent and dynamic spectral optimization theory. Our method yields preferable results compared with previous approaches on a vast variety of test network data, including both real on-line networks and computer generated complex networks.

Suggested Citation

  • Zhang, Beibei & Zhou, Yadong & Xu, Xiaoyan & Wang, Dai & Guan, Xiaohong, 2016. "Dynamic structure evolution of time-dependent network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 347-358.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:347-358
    DOI: 10.1016/j.physa.2015.12.141
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    References listed on IDEAS

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    1. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    2. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    3. Gong, Maoguo & Liu, Jie & Ma, Lijia & Cai, Qing & Jiao, Licheng, 2014. "Novel heuristic density-based method for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 71-84.
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    Cited by:

    1. He, Xi-jun & Dong, Yan-bo & Wu, Yu-ying & Jiang, Guo-rui & Zheng, Yao, 2019. "Factors affecting evolution of the interprovincial technology patent trade networks in China based on exponential random graph models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 443-457.
    2. Wei, Shanting & Zhang, Zhuo & Ke, Ginger Y. & Chen, Xintong, 2019. "The more cooperation, the better? Optimizing enterprise cooperative strategy in collaborative innovation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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