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首页> 外文期刊>Personal and Ubiquitous Computing >Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people
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Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

机译：基于人们的社交行为预测机会移动社交网络中的时间中心性

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摘要

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes' future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes' future centrality is highly predictable due to natural social behavior of people. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes' future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.
机译：对于机会移动社交网络（OMSN）中的不同应用程序，预测节点的中心性是一个重要的问题。但是，在计算此类指标时，当前的研究重点是分析不会随时间变化或在一段时间内使用汇总的联系信息的静态网络。此外，过去测度的中心性未得到验证是否可用作未来的预测指标。在本文中，为了捕获人的动态行为，我们专注于使用OMSN中的真实移动性轨迹从时间角度预测节点的未来中心性（重要性）。考虑了三个重要的中心性度量，即中间性，亲密性和程度中心性。通过真实的跟踪驱动模拟，我们发现由于人的自然社交行为，节点的未来中心性是高度可预测的。然后，基于仿真中的观察，我们设计了几种合理的预测方法来预测节点未来的时间中心性。最后，进行了广泛的实际跟踪驱动模拟，以评估我们提出的方法的性能。结果表明，最近加权平均法在MIT现实跟踪中表现最佳，而最近均匀平均法在Infocom 06跟踪中表现最佳。此外，我们还评估了参数m和w对提出的方法的性能的影响，并同时为每种提出的方​​法找到不同参数的适当值。

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  • 来源
    《Personal and Ubiquitous Computing》 |2016年第6期|885-897|共13页
  • 作者

    Huan Zhou; Linping Tong; Shouzhi Xu; Chungming Huang; Jialu Fan;

  • 作者单位

    College of Computer and Information Technology, China Three Gorges University, Yichang, China;

    College of Computer and Information Technology, China Three Gorges University, Yichang, China;

    College of Computer and Information Technology, China Three Gorges University, Yichang, China;

    Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;

    The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Opportunistic Mobile Social Networks; Centrality; Real mobility trace; Prediction method;

    机译：机会移动社交网络;中心性真实的移动轨迹;预测方法;

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