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Springer Graph Data Mining: Algorithm, Security and Application

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Description

Graph data mining is a vital tool for discovering useful information and knowledge from complex datasets. Because graph data can model arbitrary relationships between objects, it is used across many real-world fields, including bioinformatics, traffic networks, scientific collaboration, the world wide web, and social networks. This book addresses the specific challenges presented by the semi-structure of nodes and links. It covers essential computation tasks such as node classification, link prediction, and graph classification. By exploring various advanced techniques, this text provides a deep look into how graph data mining can be applied to manage big data effectively. Whether you are studying social network structures or scientific collaboration patterns, this resource provides the technical foundation needed to handle complex graph-based information.

Key Features

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

Covers essential computation tasks including node classification and link prediction.

Provides technical insights into graph classification and managing semi-structured data.

Examines how to discover useful information and knowledge from complex graph data.

Addresses the challenges of nodes and links within big data management contexts.

Product Specifications

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

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