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Springer Graph Data Mining - Algorithm and Security Book

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Description

Graph data mining is essential for discovering knowledge from complex relationship models. This Springer publication explores how to extract useful information from graph data used in bioinformatics, traffic networks, social networks, and the World Wide Web. The text addresses the computational challenges posed by nodes, links, and semi-structured data formats. Readers will examine advanced techniques designed to handle tasks such as node classification, link prediction, and graph classification. As part of the Big Data Management series, this book provides a deep look into the algorithms and security aspects required to manage large-scale graph datasets effectively.

Key Features

This Springer book covers graph data mining techniques for discovering knowledge from complex relationship models.

The content addresses computational challenges involving nodes, links, and semi-structure in graph data.

Learn about specific tasks including node classification, link prediction, and graph classification.

Explore real-world applications in fields like bioinformatics, social networks, and traffic networks.

Part of the Big Data Management series focused on advanced graph data algorithms and security.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
17 July 2022
Listed Since
19 June 2022

Barcode

No barcode data available

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