一种新的基于节点重要性的免疫策略研究

发布时间：2018-10-14 14:18

【摘要】：为了抑制病毒在网络中快速爆发,快速有效的免疫策略将有助于减少病毒带来的巨大损失,随机免疫、目标免疫、熟人免疫以及多种改进的免疫策略已经被提出.目前基于节点重要性的免疫策略主要关注该节点的度大小,而忽略了与其相邻的不同节点的重要性并不相同.基于节点的重要性提出一种改进的免疫策略——基于节点度与聚类系数的病毒免疫算法(Virus immunization based on degree and clustering coefficient of node,IDCC).通过考虑节点的度信息和与其邻居节点间的连接紧密程度计算节点重要性,选择用聚类系数表示连接紧密程度,并计算节点的度大小与聚类系数之和,选择和值较大的节点进行免疫.在人工合成网络和真实的大学邮件网络实现免疫模型并记录感染的节点数目.实验结果表明,使用IDCC免疫策略后,更能抑制病毒传播,且在免疫比例低于20%时,IDCC免疫策略效率最高.
[Abstract]:In order to suppress the rapid outbreak of virus in the network, rapid and effective immunization strategy will help to reduce the huge losses caused by the virus. Random immunization, target immunization, acquaintance immunization and many improved immunization strategies have been proposed. At present, the immune strategy based on the importance of the node focuses on the degree of the node, but neglects the different importance of the adjacent nodes. Based on the importance of nodes, an improved immune strategy named (Virus immunization based on degree and clustering coefficient of node,IDCC (virus immune algorithm based on node size and clustering coefficient) is proposed. The importance of nodes is calculated by considering the degree information of nodes and the tightness degree between nodes and their neighbors. The clustering coefficient is chosen to express the degree of connection, and the sum of degree and clustering coefficient is calculated. Select the node with higher value to be immunized. The immune model is implemented in synthetic networks and real college mail networks and the number of infected nodes is recorded. The results showed that the IDCC immunization strategy could inhibit the transmission of the virus more effectively, and the efficiency of the IDCC immunization strategy was the highest when the immune ratio was lower than 20%.
【作者单位】：安徽大学计算机科学与技术学院;安徽大学协同创新中心;
【基金】：国家高技术研究发展计划(“863”计划)(2015AA124102) 国家自然科学基金(61402006,61175046) 安徽省自然科学基金(1508085MF113) 安徽省高等学校省级自然科学基金重点项目(KJ2013A016) 教育部留学回国人员科研启动基金(第49批)
【分类号】：O157.5

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