Abstract
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.
Highlights
Nowadays, from various telecommunication systems to power grid systems, it can be seen that everyone’s lives are affected and dominated by today’s real-world complex networks [1,2,3,4,5]
Artificial intelligence emerging in recent years aims to solve the difficult problems using machine learning such as the generative adversarial network (GAN) [13]. e ideal of the generative adversarial networks (GANs) is to optimize the results of the generator through constant confrontation between the generator and the Scientific Programming discriminator. e process of generator optimization could be regarded as a kind of generative adversarial learning [14, 15]. erefore, with the mature complex network theory and new artificial intelligence methods, the problems of the influential nodes’ identification could be solved efficiently
Two methods are compared with our Internet Topology Dynamic Propagation Model (ITDPM) to identify the influential nodes that make the attack propagate as quickly as possible: the random identification (RI) and the maximum out-degree identification (MDI). e RI selects n nodes randomly as the root nodes to propagate the attack information. e MDI is to select the top-n nodes with the largest out-degree. e number of influential nodes is limited to 0.5% of the total number of nodes in the network topology. erefore, in the simulated network topology, n is NS-3 data CAIDA data
Summary
From various telecommunication systems to power grid systems, it can be seen that everyone’s lives are affected and dominated by today’s real-world complex networks [1,2,3,4,5]. Identifying the influential source nodes that make the information propagates as quickly as possible is one of the most important problems in network science, and many researchers did hard works on finding such influential nodes [6,7,8,9,10,11]. Research on influential nodes’ identification has to be based on the clear network structure, and the optimal influence problem is shown to be NP-hard [12]. Artificial intelligence emerging in recent years aims to solve the difficult problems using machine learning such as the generative adversarial network (GAN) [13]. Erefore, with the mature complex network theory and new artificial intelligence methods, the problems of the influential nodes’ identification could be solved efficiently Artificial intelligence emerging in recent years aims to solve the difficult problems using machine learning such as the generative adversarial network (GAN) [13]. e ideal of the GAN is to optimize the results of the generator through constant confrontation between the generator and the Scientific Programming discriminator. e process of generator optimization could be regarded as a kind of generative adversarial learning [14, 15]. erefore, with the mature complex network theory and new artificial intelligence methods, the problems of the influential nodes’ identification could be solved efficiently
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