Condition: 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 . Seller Inventory # 463822845

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Graph Data Mining

Qi Xuan

Published by Springer Nature Singapore Jul 2021, 2021

ISBN 10: 9811626081 ISBN 13: 9789811626081

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

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 260 pp. Englisch. Seller Inventory # 9789811626081

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Graph Data Mining : Algorithm, Security and Application

Qi Xuan

Published by Springer Nature Singapore, Springer Nature Singapore, 2021

ISBN 10: 9811626081 ISBN 13: 9789811626081

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Seller: AHA-BUCH GmbH, Einbeck, Germany

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Buch. Condition: 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. Seller Inventory # 9789811626081

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