计算机与现代化

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基于PageRank的在线社交网络消息传播模型

  

  1. (华南理工大学数学学院,广东 广州 510640)
  • 收稿日期:2016-06-13 出版日期:2017-01-12 发布日期:2017-01-11
  • 作者简介:陈皋(1990-),男,湖北黄冈人,华南理工大学数学学院硕士研究生,研究方向:数据挖掘,复杂网络,机器学习; 吴广潮(1972-),男,广东潮阳人,副教授,硕士生导师,研究方向:机器学习,数据挖掘。

Information Propagation Model in Online Social Network Based on PageRank

  1. (School of Mathematics, South China University of Technology, Guangzhou 510640, China)
  • Received:2016-06-13 Online:2017-01-12 Published:2017-01-11

摘要: 经典的消息传播模型没有充分考虑在线社交网络的复杂性以及网络节点间的拓扑结构差异。针对这种情况,提出一种基于PageRank的在线社交网络的消息传播模型P-SIR。该模型利用节点的PageRank值作为节点权威度并考虑在线社交网络传播机理,刻画不同类型节点随着时间变化的状态演化关系,反映消息传播过程受到网络拓扑结构和传播机理的影响。该模型还考虑在线社交网络中影响消息传播过程中的一些实际因素,动态指定节点的权威度以适应非均质网络,并考虑外部社会加强效应。采用3种不同类型的网络模拟消息传播过程,通过仿真实验验证P-SIR模型可以有效反映在线社交网络中的消息传播过程。

关键词: 在线社交网络, 消息传播模型, PageRank, 复杂网络, 非均质网络

Abstract: Classical information propagation models do not fully consider the complexity of online social networks and the differences of network topology structure between nodes. This paper proposed a new information propagation model in online social network based on PageRank (P-SIR). The model used PageRank of the node as a node authority and considered some transmission mechanisms in online social networks. It depicted the states evolution relationship between different types of nodes over time and reflected the news propagation process which was affected by the network topology structure and communication mechanism. The model also considered some actual factors in online social network which influenced the spreading of news. Using three different types of network to simulate the transmission process and analyze some impact factors, P-SIR model is verified by simulation experiment that it can effectively reflect the news propagation process of online social networks.

Key words: online social networks, information propagation model, PageRank, complex network, heterogeneous network

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