Graph Data Mining Algorithm, Security and Application

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  • Engels
  • Paperback
  • 9789811626111
  • 17 juli 2022
  • 243 pagina's
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Samenvatting

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.

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.

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Inhoud

Taal
en
Bindwijze
Paperback
Oorspronkelijke releasedatum
17 juli 2022
Aantal pagina's
243

Betrokkenen

Hoofdredacteur
Qi Xuan
Tweede Redacteur
Zhongyuan Ruan
Hoofduitgeverij
Springer Verlag, Singapore

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Editie
1st ed. 2021
Product breedte
155 mm
Product lengte
235 mm
Studieboek
Nee
Verpakking breedte
155 mm
Verpakking hoogte
235 mm
Verpakking lengte
235 mm
Verpakkingsgewicht
403 g

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EAN
9789811626111

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