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Hidden Link Prediction in Stochastic Social Networks (Advances in Social Networking and Online Communities) - Hardcover
Synopsis
Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types.
Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.
"synopsis" may belong to another edition of this title.
- PublisherInformation Science Reference
- Publication date2019
- ISBN 10 152259096X
- ISBN 13 9781522590965
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages300
- EditorPandey Babita, Khamparia Aditya
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Hidden Link Prediction in Stochastic Social Networks (Advances in Social Networking and Online Communities)
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Hidden Link Prediction in Stochastic Social Networks Advances in Social Networking and Online Communities
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Hidden Link Prediction in Stochastic Social Networks (Advances in Social Networking and Online Communities)
Published by
Information Science Reference, 2019
ISBN 10: 152259096X
ISBN 13: 9781522590965
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Hidden Link Prediction in Stochastic Social Networks
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Condition: New. KlappentextrnrnLink prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as re. Seller Inventory # 448011631
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Hidden Link Prediction in Stochastic Social Networks
Published by
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ISBN 10: 152259096X
ISBN 13: 9781522590965
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers. Seller Inventory # 9781522590965
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Hidden Link Prediction in Stochastic Social Networks
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ISBN 10: 152259096X
ISBN 13: 9781522590965
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