Computation in Complex Networks
Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, su...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Pizzuti, Clara |4 edt | |
700 | 1 | |a Socievole, Annalisa |4 edt | |
700 | 1 | |a Pizzuti, Clara |4 oth | |
700 | 1 | |a Socievole, Annalisa |4 oth | |
245 | 1 | 0 | |a Computation in Complex Networks |
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520 | |a Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine | ||
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653 | |a coupled map lattice | ||
653 | |a nilpotent matrix | ||
653 | |a community detection | ||
653 | |a membrane algorithm | ||
653 | |a self-organizing map network | ||
653 | |a complex networks | ||
653 | |a optimization | ||
653 | |a structural balance | ||
653 | |a minimum memory based sign adjustment | ||
653 | |a social networks | ||
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653 | |a convergence | ||
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653 | |a cloud computing architecture | ||
653 | |a service-oriented modeling | ||
653 | |a semantic search framework | ||
653 | |a QoS-based service selection | ||
653 | |a cascading failures | ||
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653 | |a machine learning | ||
653 | |a bridging centrality | ||
653 | |a disjoint nodes | ||
653 | |a disjunct nodes | ||
653 | |a node similarity | ||
653 | |a overlapping nodes | ||
653 | |a Bayesian networks | ||
653 | |a entropy | ||
653 | |a socio-ecological system | ||
653 | |a complex network | ||
653 | |a chaotic time series | ||
653 | |a Gaussian mixture model | ||
653 | |a maximum mean discrepancy | ||
653 | |a angiogenesis | ||
653 | |a network properties | ||
653 | |a variational inference | ||
653 | |a graph neural network | ||
653 | |a variational autoencoder | ||
653 | |a network embedding | ||
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653 | |a social media | ||
653 | |a information spreading | ||
653 | |a information diffusion | ||
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653 | |a language development | ||
653 | |a multilayer complex networks | ||
653 | |a stability | ||
653 | |a spreading control | ||
653 | |a graph neural networks | ||
653 | |a node classification | ||
653 | |a active learning | ||
653 | |a graph representation learning | ||
653 | |a n/a | ||
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856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76760 |7 0 |z DOAB: description of the publication |