Applied Graph Data Science
Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
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69 259 Ft
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Product details:
- Publisher Elsevier Science
- Date of Publication 28 May 2025
- ISBN 9780443296543
- Binding Paperback
- No. of pages314 pages
- Size 276x216 mm
- Weight 880 g
- Language English 665
Categories
Long description:
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.
MoreTable of Contents:
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
