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Elsevier Principles of Big Graph: In-depth Insight Volume 128

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Description

Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computers series from Elsevier, presents cutting-edge research on large-scale graph processing and analysis. This academic volume explores practical solutions for handling massive graph datasets, including centred subgraph data matrix representation techniques, NoSQL database solutions optimised for big graph applications, and deep learning approaches to graph analysis. The book covers essential topics such as graph clustering quality assessment, protein-coded gene data filtering using bloom filters, and techniques for efficiently processing large graphs. Ideal for researchers, computer scientists, and software architects working with complex data structures, this comprehensive resource bridges theory and practical implementation in modern graph computing.

Key Features

Covers CESDAM centred subgraph data matrix methods for representing and managing large graphs efficiently.

Examines NoSQL database solutions specifically evaluated for big graph application performance and scalability.

Explores deep learning techniques applied to empirical big graph analysis and data pattern recognition.

Discusses graph clustering quality metrics and correlation analysis for evaluating clustering accuracy.

Presents geneBF gene filtering approach using bloom filters for protein-coded graph data optimisation.

Addresses processing strategies for large graphs in modern computing environments and applications.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
26 January 2023
Listed Since
31 August 2022

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