Applied Graph Data Science

Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
Herausgeber: Raj, Pethuru; Zaitsev, Dmitry A; Song, Houbing Herbert; Chong, Peter Han Joo; Dutta, Pushan Kumar

• Broschiertes Buch

Produktbeschreibung

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases 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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 250
• Erscheinungstermin: 1. Februar 2025
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443296543
• ISBN-10: 0443296545
• Artikelnr.: 70983196

Inhaltsangabe

1. Introduction to Graph neural network: A systematic review of trends,
methods, and applications
2. Chronological Reasoning in Knowledge Graphs using AI and ML: A novel
framework
3. Graph-based Approach on Financial Fraudulent Detection and Prediction
4. The Power of Graph Neural Networks: From Theory to Application
5. Delineating Graph Neural Networks (GNNs) and the Real-World Applications
6. Graph Techniques for Enhancing Knowledge Graph Integration: A
Comprehensive Study and Applications
7. Graphs, Language Models, and NLP: The Future of Search Engines
8. Graph Data Science and ML techniques: Applications and future
9. Innovative Feature Engineering Methods for Graph Data Science
10. Graph Neural Networks: Insight and Applications
11. Graph-Theoretic Analysis for Eco-Efficient Textile Weaving Patterns
12. Quantum-assisted Graph Networks: Algorithmic Innovations and
Optimization Strategies for Large-Scale Social Communities
13. Using physics-informed AI and graph-based quantum computing for natural
catastrophic analysis: Future perspectives
14. Integrating Machine Learning and Deep Learning Algorithms in Knowledge
Graph for Disease Screening and Cataloging: Tools and Approaches for Drug
Invention and Additive Manufacturing
15. Analysing Social network with dynamic graphs: unravelling the
ever-evolving connection
16. Transforming E-commerce with Graph Neural Networks: Enhancing
Personalization, Security, and Business Growth
17. On Rings Domination in Soft Graphs
18. Graph Data Science: Applications and Future
19. Verification of MPI programs via compilation into Petri nets
20. Demonstration and Analysis of the Performance of Image Caption
Generator: An Effort for Visually Impaired Candidates for Smart Cities 5.0