计算机应用 ›› 2014, Vol. 34 ›› Issue (2): 411-416.

• 数据技术 • 上一篇    下一篇

在线社交网络下基于信任度的消息传播模型

张晓伟   

  1. 广东工程职业技术学院 计算机信息系,广州 410138
  • 收稿日期:2013-07-26 修回日期:2013-09-11 出版日期:2014-02-01 发布日期:2014-03-01
  • 通讯作者: 张晓伟
  • 作者简介:张晓伟(1974-),男,广东广州人,高级工程师,硕士,主要研究方向:计算机网络安全、人工智能。
  • 基金资助:
    广东省自然科学基金项目

Trust-based information propagation model in online social networks

ZHANG Xiaowei   

  1. Department of Computer Information, Guangdong Engineering Polytechnic, Guangzhou Guangdong 410138, China
  • Received:2013-07-26 Revised:2013-09-11 Online:2014-02-01 Published:2014-03-01
  • Contact: ZHANG Xiaowei
  • Supported by:
    the Guangdong Provincial Natural Science Foundation of China

摘要: 社交网络作为一种新兴的媒体具有广泛的社会影响力,且基于社交网络的营销方式逐渐成为一种新的发展趋势,因此研究社交网络中消息的传播具有重大的现实和经济意义。通过借鉴日常生活中人与人之间的信任原理,提出了一种基于信任度的消息传播模型。该模型首先利用个体的公开信息,使用数据挖掘的算法对个体进行分类;然后,根据同类和不同类个体之间的关系计算个体之间的信任度;最后,使用消息与个体的属性相似性以及信任度来计算消息可能传播范围。给出了相应的计算方法,并与两种基准方法对比,结果表明,该模型在准确度上提升15%左右,而所用时间降低50%以上。与数据集统计结果对比,该实验的结果与统计结果相差5%左右,充分表明该模型在实际应用中有比较好的效果。

关键词: 社交网络, 数据挖掘, 分类算法, 信任度, 网络的结构聚类算法, 消息传播模型

Abstract: As a new media, social network gains a wide range of social influence, and the social network based e-commerce becomes more and more popular, which make the study of information propagation of great significance. A trust based information propagation model was presented in this paper according to the trust relationship between people in daily life. First, the algorithm of data mining was employed on personal information to make the classification of the users. And then the value of trust between the users was calculated according to their relationships. At last, this paper predicted the range of information propagation by using the trust value between the users and messages. Compared with two basic methods, the final experiment shows that the results generated by the model are enhanced by 15% in precision, while the time used decreases more than 50%. The results differ with the statistic results on the dataset at 5%, which shows that the model preforms well in practice.

Key words: social network, data mining, classification algorithm, trust, Structural Clustering Algorithm for Networks (SCAN), information propagation model

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