计算机与现代化

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一种复杂网络上信息传播的动态竞争机制

  

  1. 1.北京航空航天大学数学与系统科学学院,北京100191;2.北京航空航天大学数学、信息与行为教育部重点实验室,北京100191;
    3.北京大学数学科学学院,北京100871;4.首都经济贸易大学统计学院,北京100070
  • 收稿日期:2015-11-11 出版日期:2016-01-22 发布日期:2016-01-26
  • 作者简介:李甍(1988-),男,黑龙江哈尔滨人,北京航空航天大学数学与系统科学学院博士研究生,研究方向:复杂网络结构与传播,网络同步动力学; 马一方(1988-),男,陕西韩城人,北京大学数学科学学院博士研究生,研究方向:网络结构化与数据处理。
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(11401396); 北京航空航天大学基本科研业务费-博士研究生创新基金资助项目

Competition Dynamics of Information Propagation on Complex Network

  1. 1. School of Mathematics and Systems Science, Beihang University, Beijing 100191, China;
    2. Key Laboratory of Mathematics, Informations and Behavioral Semantics, Ministry of Education of the People’s Republic of China, Beihang University, Beijing 100191, China; 
    3. School of Mathematical Sciences, Peking University, Beijing 100871, China;
    4. School of Statistics, Capital University of Economics and Business, Beijing 100070, China
  • Received:2015-11-11 Online:2016-01-22 Published:2016-01-26

摘要: 非共识性的意见普遍存在于人类的交互活动中。本文研究由带有反对意见的信息诱导出的信息竞争动态过程。建立一个新的信息竞争模型,以体现复杂网络中消息交互的真实情况。结果显示,对网络中节点进行k-shell分类后发现,节点的k-shell值在信息竞争过程中起到了重要作用。通过变换模型中的参数,可以成功解释不同的信息竞争现象。新的信息竞争模型非常适用于对网络信息传播的研究。

关键词: 信息竞争, k-shell分解, 复杂网络

Abstract: The “non-consensus” opinions pervade all patterns of human’s interactive activities. This paper studies the phenomenon of the information competition dynamics induced by messages with opposite meaning. We construct a new information competition model which could perfectly match the reality on complex networks. It is shown that the k-shell value of nodes plays an important role in reflecting its competitive power in the information competition processes, after classifying the nodes on the network with the k-shell decomposition technique. By varying the variables in the model, different cases of information competition phenomena can be successfully explained. This new information competition model should be useful to the study on network information dissemination.

Key words: information competition, k-shell decomposition, complex network

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