文章基本信息

标题：ITDPM: An Internet Topology Dynamic Propagation Model Based on Generative Adversarial Learning
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作者：Hangyu Hu ; Xuemeng Zhai ; Gaolei Fei 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2021
卷号：2021
页码：1-9
DOI：10.1155/2021/2390466
出版社：Hindawi Publishing Corporation
摘要：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.