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Limitation of degree information for analyzing the interaction evolution in online social networks

Author

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  • Ke-Ke Shang

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, P. R. China)

  • Wei-Sheng Yan

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, P. R. China)

  • Xiao-Ke Xu

    (College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116605, P. R. China)

Abstract

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

Suggested Citation

  • Ke-Ke Shang & Wei-Sheng Yan & Xiao-Ke Xu, 2014. "Limitation of degree information for analyzing the interaction evolution in online social networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(10), pages 1-10.
    • Handle: RePEc:wsi:ijmpcx:v:25:y:2014:i:10:n:s0129183114500569
    DOI: 10.1142/S0129183114500569
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    Cited by:

    1. Zhang, Yaotian & Feng, Mingming & Shang, Ke-ke & Ran, Yijun & Wang, Cheng-Jun, 2022. "Peeking strategy for online news diffusion prediction via machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Shang, Ke-ke & Yan, Wei-sheng & Small, Michael, 2016. "Evolving networks—Using past structure to predict the future," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 120-135.

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