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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Year of Publication: | 2021 |
Language: | English |
Physical Description: | 1 electronic resource (352 p.) |
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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 city interaction network | ||
653 | |a evolution model | ||
653 | |a preferential attachment | ||
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653 | |a optimization | ||
653 | |a structural balance | ||
653 | |a minimum memory based sign adjustment | ||
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653 | |a NW network | ||
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653 | |a cloud computing architecture | ||
653 | |a service-oriented modeling | ||
653 | |a semantic search framework | ||
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653 | |a maximum mean discrepancy | ||
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653 | |a variational inference | ||
653 | |a graph neural network | ||
653 | |a variational autoencoder | ||
653 | |a network embedding | ||
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653 | |a computational biology | ||
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653 | |a protein contact networks | ||
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653 | |a renormalisation process | ||
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653 | |a inverse preferential attachment | ||
653 | |a language networks | ||
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 | ||
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