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Measures of node centrality in mobile social networks

Author

Listed:
  • Zhenxiang Gao

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China)

  • Yan Shi

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China)

  • Shanzhi Chen

    (State Key Laboratory of Wireless Mobile Communications, China Academy of Telecommunications Technology, Beijing 100083, P. R. China)

Abstract

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

Suggested Citation

  • Zhenxiang Gao & Yan Shi & Shanzhi Chen, 2015. "Measures of node centrality in mobile social networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(09), pages 1-20.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:09:n:s0129183115501077
    DOI: 10.1142/S0129183115501077
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    Cited by:

    1. Bi, Jialin & Jin, Ji & Qu, Cunquan & Zhan, Xiuxiu & Wang, Guanghui & Yan, Guiying, 2021. "Temporal gravity model for important node identification in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Meng, Yanhong & Yi, Yunhui & Xiong, Fei & Pei, Changxing, 2019. "T×oneHop approach for dynamic influence maximization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 575-586.
    3. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.

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