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Ajouter au panierEtat : New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Ajouter au panierTaschenbuch. Etat : Neu. Graph Data Mining | Algorithm, Security and Application | Qi Xuan (u. a.) | Taschenbuch | xvi | Englisch | 2022 | Springer Singapore | EAN 9789811626111 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Langue: anglais
Edité par Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10 : 9811626111 ISBN 13 : 9789811626111
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic - the security of graph data mining - and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.
Langue: anglais
Edité par Springer Nature Singapore, Springer Nature Singapore, 2021
ISBN 10 : 9811626081 ISBN 13 : 9789811626081
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic - the security of graph data mining - and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.
Langue: anglais
Edité par Springer-Nature New York Inc, 2021
ISBN 10 : 9811626081 ISBN 13 : 9789811626081
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Ajouter au panierHardcover. Etat : Brand New. 259 pages. 9.25x6.10x0.63 inches. In Stock.
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive overview of the state-of-the-art in graph data mining algorithmsIntroduces various key applications of the advanced graph data mining techniquesPresents robust graph data mining based on subgraph networks and graph .
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Ajouter au panierBuch. Etat : Neu. Graph Data Mining | Algorithm, Security and Application | Qi Xuan (u. a.) | Buch | xvi | Englisch | 2021 | Springer Singapore | EAN 9789811626081 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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