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Springer Graph Data Mining - Algorithms and Applications

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Description

Graph data mining provides the tools needed to extract knowledge from complex relationship models. This Springer publication explores how to manage and analyze graph data used in bioinformatics, social networks, traffic networks, and the World Wide Web. Because graph data involves semi-structured nodes and links, it presents unique computational challenges. This book addresses essential tasks including node classification, link prediction, and graph classification. It covers advanced techniques for big data management, helping readers navigate the complexities of modeling arbitrary relationships between objects in various real-world scientific and digital environments.

Key Features

This Springer book covers graph data mining for big data management applications.

Learn to model arbitrary relationships between objects in fields like bioinformatics and social networks.

Explore computational tasks such as node classification and link prediction.

Address the challenges of semi-structure, nodes, and links in graph data.

Study graph classification techniques used in traffic networks and the World Wide Web.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
16 July 2021
Listed Since
13 April 2021

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