Network information propagation analysis is gaining a more important role in network vulnerability analysis domain for preventing potential risks and threats. Identifying the influential source nodes is one of the most important problems to analyze information propagation. Traditional methods mainly focus on extracting nodes that have high degrees or local clustering coefficients. However, these nodes are not necessarily the high influential nodes in many real-world complex networks. Therefore, we propose a novel method for detecting high influential nodes based on Internet Topology Dynamic Propagation Model (ITDPM). The model consists of two processing stages: the generator and the discriminator like the generative adversarial networks (GANs). The generator stage generates the optimal source-driven nodes based on the improved network control theory and node importance characteristics, while the discriminator stage trains the information propagation process and feeds back the outputs to the generator for performing iterative optimization. Based on the generative adversarial learning, the optimal source-driven nodes are then updated in each step via network information dynamic propagation. We apply our method to random-generated complex network data and real network data; the experimental results show that our model has notable performance on identifying the most influential nodes during network operation.

中文翻译：

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ITDPM：基于生成对抗学习的互联网拓扑动态传播模型

在网络漏洞分析领域，网络信息传播分析在防范潜在风险和威胁方面发挥着越来越重要的作用。识别有影响的源节点是分析信息传播的最重要问题之一。传统方法主要侧重于提取度数高或局部聚类系数高的节点。然而，这些节点不一定是许多现实世界复杂网络中的高影响节点。因此，我们提出了一种基于 Internet 拓扑动态传播模型 (ITDPM) 检测高影响节点的新方法。该模型由两个处理阶段组成：生成器和鉴别器，如生成对抗网络 (GAN)。生成器阶段根据改进的网络控制理论和节点重要性特征生成最优的源驱动节点，而鉴别器阶段训练信息传播过程并将输出反馈给生成器进行迭代优化。基于生成对抗学习，然后通过网络信息动态传播在每一步中更新最优源驱动节点。我们将我们的方法应用于随机生成的复杂网络数据和真实网络数据；实验结果表明，我们的模型在识别网络运行过程中最具影响力的节点方面具有显着的性能。而鉴别器阶段训练信息传播过程并将输出反馈给生成器以执行迭代优化。基于生成对抗学习，然后通过网络信息动态传播在每一步中更新最优源驱动节点。我们将我们的方法应用于随机生成的复杂网络数据和真实网络数据；实验结果表明，我们的模型在识别网络运行过程中最具影响力的节点方面具有显着的性能。而鉴别器阶段训练信息传播过程并将输出反馈给生成器以执行迭代优化。基于生成对抗学习，然后通过网络信息动态传播在每一步中更新最优源驱动节点。我们将我们的方法应用于随机生成的复杂网络数据和真实网络数据；实验结果表明，我们的模型在识别网络运行过程中最具影响力的节点方面具有显着的性能。