Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Liang XunXun Liang has worked in the fields of social networks, machine learning, and financial information systems for more than 20 years. He is the chief expert of many research and industrial projects. He has published more than 2. Seller Inventory # 530568179

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Multisource Heterogeneous Graph Big Data Representation Learning

Xun Liang

Published by LAP LAMBERT Academic Publishing Nov 2021, 2021

ISBN 10: 6204719327 ISBN 13: 9786204719320

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The large amount of accumulated and complex data also brings challenges to query and processing. With the update of data, the number of nodes and edges contained in the graph may become larger and larger. The number of nodes in large-scale graph structure data can reach millions or even hundreds of millions, and presents the characteristics of multisource, heterogeneity, isomerization and dynamics.Multisource heterogeneous big data can often be modeled into a graph data structure with representation learning. The complex network graph normally has certain particularity, which increases the difficulty of research. Large-scale complex heterogeneous graph data representation learning model has a wide range of applications in many fields. This book addresses these multisource heterogeneous graph big data representation learning models as well as their applications in the field of public security. 160 pp. Englisch. Seller Inventory # 9786204719320

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Multisource Heterogeneous Graph Big Data Representation Learning : For Public Security

Xun Liang

Published by LAP LAMBERT Academic Publishing, 2021

ISBN 10: 6204719327 ISBN 13: 9786204719320

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Seller: AHA-BUCH GmbH, Einbeck, Germany

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The large amount of accumulated and complex data also brings challenges to query and processing. With the update of data, the number of nodes and edges contained in the graph may become larger and larger. The number of nodes in large-scale graph structure data can reach millions or even hundreds of millions, and presents the characteristics of multisource, heterogeneity, isomerization and dynamics.Multisource heterogeneous big data can often be modeled into a graph data structure with representation learning. The complex network graph normally has certain particularity, which increases the difficulty of research. Large-scale complex heterogeneous graph data representation learning model has a wide range of applications in many fields. This book addresses these multisource heterogeneous graph big data representation learning models as well as their applications in the field of public security. Seller Inventory # 9786204719320

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