With the popularization of computers and the development of information engineering, the emergence of search engines makes it possible to get the information needed from big data quickly and efficiently. However, in recent years, a multiplicity of new viruses have been propagated by search engines. Many researchers choose to cut off the source of virus propagation, ignoring the virus immunization strategy based on the search engine. In this paper, we analyze the impact of search engines on virus propagation. First, considering the immune effect and cost, two kinds of immune mechanisms based on the search engine that have greater practicability are defined. Second, immune mechanisms based on the search engine are theoretically analyzed by the iteration method and the dynamic method. The results show that this immunization strategy can slow down or eliminate the propagation of a virus to a certain extent. Third, three real social network data sets are used to simulate and analyze the immune mechanism. We find that when the proportion of nodes being infected and the proportion of infected nodes being identified by the search engine satisfy a certain relationship, our immune mechanism can inhibit the spread of viruses, which confirms our theoretical analysis results.

中文翻译：

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搜索引擎对病毒传播的影响

随着计算机的普及和信息工程的发展，搜索引擎的出现使得从大数据中快速高效地获取所需信息成为可能。然而，近年来，搜索引擎传播了多种新病毒。许多研究人员选择切断病毒传播源头，忽略了基于搜索引擎的病毒免疫策略。在本文中，我们分析了搜索引擎对病毒传播的影响。首先，考虑到免疫效果和成本，定义了两种具有较大实用性的基于搜索引擎的免疫机制。其次，通过迭代法和动态法从理论上分析了基于搜索引擎的免疫机制。结果表明，这种免疫策略可以在一定程度上减缓或消除病毒的传播。第三，使用三个真实的社交网络数据集来模拟和分析免疫机制。我们发现当节点被感染的比例和被搜索引擎识别的被感染节点的比例满足一定的关系时，我们的免疫机制可以抑制病毒的传播，这印证了我们的理论分析结果。