In the information age of big data, and increasingly large and complex networks, there is a growing challenge of understanding how best to restrain the spread of harmful information, for example, a computer virus. Establishing models of propagation and node immunity are important parts of this problem. In this article, a dynamic node immune model, based on the community structure and threshold (NICT), is proposed. First, a network model is established, which regards nodes carrying harmful information as new nodes in the network. The method of establishing the edge between the new node and the original node can be changed according to the needs of different networks. The propagation probability between nodes is determined by using community structure information and a similarity function between nodes. Second, an improved immune gain, based on the propagation probability of the community structure and node similarity, is proposed. The improved immune gain value is calculated for neighbors of the infected node at each time step, and the node is immunized according to the hand-coded parameter: immune threshold. This can effectively prevent invalid or insufficient immunization at each time step. Finally, an evaluation index, considering both the number of immune nodes and the number of infected nodes at each time step, is proposed. The immune effect of nodes can be evaluated more effectively. The results of network immunization experiments, on eight real networks, suggest that the proposed method can deliver better network immunization than several other well-known methods from the literature.

中文翻译：

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基于社团结构和阈值的复杂网络动态免疫节点模型


在大数据的信息时代以及日益庞大和复杂的网络中，了解如何最好地抑制有害信息（例如计算机病毒）的传播面临着越来越大的挑战。建立传播和节点免疫模型是这个问题的重要组成部分。本文提出了一种基于社区结构和阈值（NICT）的动态节点免疫模型。首先建立网络模型，将携带有害信息的节点视为网络中的新节点。新节点与原节点之间建立边的方法可以根据不同网络的需要而改变。节点之间的传播概率是通过使用社区结构信息和节点之间的相似度函数来确定的。其次，提出了一种基于社区结构的传播概率和节点相似性的改进免疫增益。在每个时间步计算受感染节点的邻居的改进免疫增益值，并根据手工编码的参数：免疫阈值对节点进行免疫。这可以有效防止每个时间步的免疫无效或不足。最后，提出了一个同时考虑每个时间步的免疫节点数量和感染节点数量的评估指标。可以更有效地评估节点的免疫效果。在八个真实网络上的网络免疫实验结果表明，所提出的方法可以比文献中其他几种众所周知的方法提供更好的网络免疫。