Research On Network Modeling And Performance Analysis For Complex Systems

Complex systems are ubiquitous in our daily lives,including internet systems,communication systems,transportation systems,ecosystems,and so on.Research on complex systems provides new insights for tackling real-world problems,such as dealing with large-scale blackouts of power systems,designing effective network architectures of communication systems,and studying disease propagation of social systems.Besides,research on complex systems can accelerate the development of disciplines and promote the innovation of theories,involving mathematics,economics,computer science,and so on.Therefore,it is significant to delve into the study of complex systems.However,due to the large number of individuals in the system and the lack of centralized control over individual behaviors,analyzing complex systems is particularly challenging.This paper explores several problems in complex systems through complex networks and deep learning methods.The main works are described as follows:Firstly,given the lack of basic theories and simulation experiments for constructing multilayer systems in complex system research,two multilayer networks are proposed using complex network methods.The interaction of nodes within the layer and the coupling of inter-layer networks are described in detail.Moreover,considering the dynamic propagation of failure in multilayer networks,two cascading failure models are established,which are mathematically analyzed by mean-field approximation and generating function.Furthermore,the effective probabilities of nodes under different failure modes are calculated and the theoretical solutions of a stable network are obtained.Through conducting extensive experiments on cascading models with different attack intensities,the theoretical values are verified and the factors affecting network robustness are discussed.The proposed method provides a novel perspective on designing system architecture,adjusting system parameters,and enhancing system robustness.Secondly,to address the issue of neglecting individuals’geographic characteristics in the modeling process of complex systems,a multilayer spatial network model is established using complex network methods.By analyzing real-world spatial systems,the results show that the distance between individuals follows an exponential distribution.Therefore,the spatial network model utilizes the distance between nodes to characterize their geographical features and provides a detailed generation process.Moreover,considering the dynamic propagation of failure in spatial networks,two cascading failure models are proposed.The evolutions of spatial networks under different attack intensities are analyzed and the number of failure nodes under different modes is counted.Besides,the factors affecting network robustness and stability are analyzed and compared.The proposed method provides insight into supporting the research of spatial systems,including optimizing network structure,maintaining stability and robustness,and adjusting the difference degree between individuals.Thirdly,to overcome the one-sidedness and subjectivity of previous methods in identifying influential nodes,a new approach based on deep learning methods is proposed by considering the multidimensional features of nodes.The approach utilizes a contraction algorithm to obtain feature matrices of nodes,which could reflect the multidimensional information of nodes and eliminate the process of manually specifying statistical indicators as initial features.Subsequently,convolutional neural networks and graph neural networks are leveraged to capture node features and network structure information.Extensive experiments show that the proposed approach performs better than the best comparison algorithm,such as the accuracy has improved by 2%-20%,most nodes have unique ranking values with distinguishability reaching over 90%.The proposed method has significant practical value for controlling the spread of rumors,promoting the flow of information,and enhancing the security of systems.Fourthly,to tackle the issues of insufficient data and inconsistent features in the knowledge discovery of complex systems,a social recommendation algorithm based on deep learning methods is proposed by considering the system structure and the interactions between individuals.The proposed algorithm leverages social networks and interactive networks to mine various types of neighbor information for users and items,and designs a sampler to filter neighbor features.Subsequently,graph neural networks and attention mechanisms are applied to iteratively aggregate neighbor features and network structure information.Experimental results on classical datasets demonstrate that the algorithm improves prediction accuracy and reduces the maximum error.The proposed algorithm can effectively tackle information overload and enhance recommendation accuracy,demonstrating practical value for realworld applications.