Name: Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied use Cases (en Inglés)
Price: 187.30 USD
Availability: InStock
Author: Raj Pethuru,Dutta Pushan Kumar,Chong Peter Han Joo
ISBN: 9780443296543

portada Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied use Cases (en Inglés)

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied use Cases (en Inglés)

Formato

Libro Físico

Editorial

Morgan Kaufmann Publishers

Autor

Raj Pethuru,Dutta Pushan Kumar,Chong Peter Han Joo

Idioma

Inglés

N° páginas

314

Encuadernación

Tapa Blanda

ISBN13

9780443296543

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied use Cases (en Inglés)

Raj Pethuru,Dutta Pushan Kumar,Chong Peter Han Joo (Autor) · Morgan Kaufmann Publishers · Tapa Blanda

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Reseña del libro "Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied use Cases (en Inglés)"

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.