戴云晶, 邓倩妮. 在线社交网络用户间影响量化的贝叶斯模型[J]. 微电子学与计算机, 2013, 30(3): 106-109.
引用本文: 戴云晶, 邓倩妮. 在线社交网络用户间影响量化的贝叶斯模型[J]. 微电子学与计算机, 2013, 30(3): 106-109.
DAI Yun-jing, DENG Qian-ni. Pairwise Influence Quantification Bayes Model for Online Social Network[J]. Microelectronics & Computer, 2013, 30(3): 106-109.
Citation: DAI Yun-jing, DENG Qian-ni. Pairwise Influence Quantification Bayes Model for Online Social Network[J]. Microelectronics & Computer, 2013, 30(3): 106-109.

在线社交网络用户间影响量化的贝叶斯模型

Pairwise Influence Quantification Bayes Model for Online Social Network

  • 摘要: 传统的模型专注于合作网络等社交网络,并不完全适用于在线社交网络.而现有的针对在线社交网络的模型简单,片面,缺乏准确性.因此我们提出了基于贝叶斯观点的模型,通过集成在线社交网络提供的有用信息来量化用户间的影响.通过分析对比贝叶斯模型与现有模型找到的影响者,我们发现贝叶斯模型比现有的模型更准确和全面.

     

    Abstract: Traditional researches of pairwise influence quantification are focus on social network such as coauthor network.So they don't fully apply to current online social network.Furthermore,existing model which are proposed for online social network are simple,one-sided and lack of accuracy.So we propose a Bayes model which integrated all useful information online social network provides to quantify the pairwise influence between two users.Through analyzing and comparing the influential found by these models,we find that our model is more accurate and comprehensive than existing models.

     

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