中文摘要：

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.

英文摘要：

In order to effectively restrain the rapid propagating of virus on the network,fast and effective immune strategies are studied to reduce the losses brought by virus.Random immunization,targeted immunization,and acquaintance immunization as well as other improved immune strategies have been proposed.Under current circumstances,immune strategy based on the importance of node mainly focuses on its degree but ignores the fact that the importance of different neighbor node is not the same.Based on the importance of the node,the paper proposes a novel immune strategy,named virus immunization based on degree and clustering coefficient of node（IDCC）.IDCC Algorithm not only calculate the node importance by completely considering the total information of node degree,but also compute the closeness of how it connect with the adjacent node.The paper chooses clustering coefficient to show the closeness and sum the degree and clustering coefficient,of which the max is chosen to conduct immunization.Soas to better prove the validity of the experiment,immunization model is implemented in the synthetic network as well as real university mail network and the amount of infectious nodes is recorded.The experiment result shows that IDCC can better restrain the virus propagate and exerts the highest efficiency when the immunization proportion is below 20%.