2019
DOI: 10.1063/1.5121401
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Learning epidemic threshold in complex networks by Convolutional Neural Network

Abstract: Deep learning has taken part in the competition since not long ago to learn and identify phase transitions in physical systems such as many body quantum systems, whose underlying lattice structures are generally regular as they're in euclidean space. Real networks have complex structural features which play a significant role in dynamics in them, and thus the structural and dynamical information of complex networks can not be directly learned by existing neural network models. Here we propose a novel and effec… Show more

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Cited by 15 publications
(3 citation statements)
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“…Neural network surface reconstruction has the advantages of high fitting accuracy to the measurement data, fewer surface patches, and editing and modification of local surfaces, but the problem is how to choose network parameters reasonably, solving the contradiction between network training speed and approximation accuracy, and solving problems such as continuous splicing which deserves further study. erefore, in computer 3D clothing design including 2D and 3D mutual conversion, there is a need to do a study of fabric texture mapping, analysis of optical and mechanical properties, and 3D interactive design [11]. Bangari et al found that many institutions at home and abroad are currently engaged in the research and development of 3D CAD apparel.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Neural network surface reconstruction has the advantages of high fitting accuracy to the measurement data, fewer surface patches, and editing and modification of local surfaces, but the problem is how to choose network parameters reasonably, solving the contradiction between network training speed and approximation accuracy, and solving problems such as continuous splicing which deserves further study. erefore, in computer 3D clothing design including 2D and 3D mutual conversion, there is a need to do a study of fabric texture mapping, analysis of optical and mechanical properties, and 3D interactive design [11]. Bangari et al found that many institutions at home and abroad are currently engaged in the research and development of 3D CAD apparel.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In the field of network epidemiology, since the coupling relationship between the underlying network topology and dynamic results is hard to express explicitly, machine learning provides a key to approximating it. Recently, there have been some interdisciplinary works on integrating network epidemiology and machine learning that provide a new way to solve challenges, such as epidemic threshold identification [15,16], basic reproduction number prediction [17], source tracing [18], state transition probability estimation [19], and individual's health state inferences [20].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, ML techniques have been proposed to study the epidemic cluster in the susceptibleinfectious-susceptible (SIS) compartmental model of epidemic spreading on networks [40].…”
Section: Introductionmentioning
confidence: 99%