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链路预测的网络演化模型评价方法 被引量：4

New Method of Assessing Network Evolving Models Based on Link Prediction
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摘要 在网络演化研究领域,以前工作中对于网络演化机制之间的比较并没有公平、统一的标准。该文基于链路预测理论,采用极大似然估计思想建立了一套用于评价网络演化模型的体系。在基于自治系统的数据实验中,比较了GLP和Tang两个演化模型,结果显示GLP优于Tang,而且得到的最优参数也与其提出者给出的均不相同。实验结果表明基于一定规模为真实网络使用新参数生成的网络更加接近真实网络,并且本文的评价框架可以为模型参数的选取提供建议。 As the previous evaluation methods of evolving models can not be credible because of its inconformity, in this paper, we propose a new method by applying the theory of link prediction and maximum likelihood estimation. Based on the Internet autonomous system networks, we find that GLP is better than Tang which is not agreed with previous results. Moreover, the optimal parameters of these two evolving models are different from the original ones. The experimental results support our optimal parameters with which the corresponding models can generate more real networks.
作者 王文强 张千明
机构地区 电子科技大学互联网科学中心
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2011年第2期174-179,共6页 Journal of University of Electronic Science and Technology of China
基金 国家自然科学基金(11075031)
关键词 评价方法 链路预测:极大似然估计 网络演化模型 evaluation link prediction maximum likelihood estimation network evolving model
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献19

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共引文献243

同被引文献117

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引证文献4

二级引证文献32

电子科技大学学报
2011年 第2期

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