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Exploring the evolution of node neighborhoods in Dynamic Networks

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  • Orman, Günce Keziban
  • Labatut, Vincent
  • Naskali, Ahmet Teoman

Abstract

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

Suggested Citation

  • Orman, Günce Keziban & Labatut, Vincent & Naskali, Ahmet Teoman, 2017. "Exploring the evolution of node neighborhoods in Dynamic Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 375-391.
  • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:375-391
    DOI: 10.1016/j.physa.2017.04.084
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    References listed on IDEAS

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    1. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
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    Cited by:

    1. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    2. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    3. 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.
    4. Tian, Ru-Ya & Wu, Lei & Liang, Xiao-He & Zhang, Xue-Fu, 2018. "Opinion data mining based on DNA method and ORA software," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1471-1480.
    5. Wei, Daijun & Zhang, Xiaoge & Mahadevan, Sankaran, 2018. "Measuring the vulnerability of community structure in complex networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 41-52.

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