The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent...The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent times. In practice, networks may be very large, and data can often be measured for only some of the nodes in a network while data for other variables are hidden.It is thus crucial to be able to infer networks from partial data. In this article, we study the problem of noise-driven nonlinear networks with some hidden nodes. Various difficulties appear jointly: nonlinearity of network dynamics, the impact of strong noise, the complexity of interaction structures between network nodes, and missing data from certain hidden nodes. We propose using high-order correlation to treat nonlinearity and structural complexity, two-time correlation to decorrelate noise, and higherorder derivatives to overcome the difficulties of hidden nodes. A closed form of network reconstruction is derived, and numerical simulations confirm the theoretical predictions.展开更多
Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong inte...Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.11135001)China Postdoctoral Science Foundation(Grant No.2015M581905)
文摘The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent times. In practice, networks may be very large, and data can often be measured for only some of the nodes in a network while data for other variables are hidden.It is thus crucial to be able to infer networks from partial data. In this article, we study the problem of noise-driven nonlinear networks with some hidden nodes. Various difficulties appear jointly: nonlinearity of network dynamics, the impact of strong noise, the complexity of interaction structures between network nodes, and missing data from certain hidden nodes. We propose using high-order correlation to treat nonlinearity and structural complexity, two-time correlation to decorrelate noise, and higherorder derivatives to overcome the difficulties of hidden nodes. A closed form of network reconstruction is derived, and numerical simulations confirm the theoretical predictions.
基金supported by the National Science and Technology Major Project of China(Grant No.2014ZX10004001-014)the National Natural Science Foundation of China(Grant Nos.61573262,61532020&11472290)the Fundamental Research Funds for the Central Universities(Grant No.2014201020206)
基金This research is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R 151)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.
