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.
1. Chapter 1. Introduction to graph neural network: A systematic review of trends, methods, and applications - p. 1 2. Chapter 2. Chronological reasoning in knowledge graphs using AI and ML: A novel framework - p. 17 3. Chapter 3. Graph based approach on financial fraudulent detection and prediction - p. 25 4. Chapter 4. The power of graph neural networks: From theory to application - p. 39 5. Chapter 5. Delineating graph neural networks (GNNs) and the real-world applications - p. 59 6. Chapter 6. Graph techniques for enhancing knowledge graph integration: A comprehensive study and applications - p. 71 7. Chapter 7. Graphs, language models, and NLP: The future of search engines - p. 91 8. Chapter 8. Graph Data Science and ML techniques: Applications and future - p. 105 9. Chapter 9. Innovative feature engineering methods for graph data science - p. 119 10. Chapter 10. Graph neural networks: Insight and applications - p. 135 11. Chapter 11. Graph-theoretic analysis for eco-efficient textile weaving patterns - p. 145 12. Chapter 12. Quantum-assisted graph networks: Algorithmic innovations and optimization strategies for large scale social communities - p. 151 13. Chapter 13. Using physics-informed AI and graph-based quantum computing for natural catastrophic analysis: Future perspectives - p. 167 14. Chapter 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 - p. 181 15. Chapter 15. Analyzing social networks with dynamic graphs: Unravelling the ever-evolving connections - p. 195 16. Chapter 16. Transforming e-commerce with Graph Neural Networks: Enhancing personalization, security, and business growth - p. 215 17. Chapter 17. On ring domination in soft graphs - p. 225 18. Chapter 18. Graph data science: Applications and future - p. 227 19. Chapter 19. Verification of MPI programs via compilation into Petri nets - p. 245 20. Chapter 20. Demonstration and analysis of the performance of image caption generator: An effort for visually impaired candidates for smart cities 5.0 - p. 271 21. Index - p. 295
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.
1. Chapter 1. Introduction to graph neural network: A systematic review of trends, methods, and applications - p. 1 2. Chapter 2. Chronological reasoning in knowledge graphs using AI and ML: A novel framework - p. 17 3. Chapter 3. Graph based approach on financial fraudulent detection and prediction - p. 25 4. Chapter 4. The power of graph neural networks: From theory to application - p. 39 5. Chapter 5. Delineating graph neural networks (GNNs) and the real-world applications - p. 59 6. Chapter 6. Graph techniques for enhancing knowledge graph integration: A comprehensive study and applications - p. 71 7. Chapter 7. Graphs, language models, and NLP: The future of search engines - p. 91 8. Chapter 8. Graph Data Science and ML techniques: Applications and future - p. 105 9. Chapter 9. Innovative feature engineering methods for graph data science - p. 119 10. Chapter 10. Graph neural networks: Insight and applications - p. 135 11. Chapter 11. Graph-theoretic analysis for eco-efficient textile weaving patterns - p. 145 12. Chapter 12. Quantum-assisted graph networks: Algorithmic innovations and optimization strategies for large scale social communities - p. 151 13. Chapter 13. Using physics-informed AI and graph-based quantum computing for natural catastrophic analysis: Future perspectives - p. 167 14. Chapter 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 - p. 181 15. Chapter 15. Analyzing social networks with dynamic graphs: Unravelling the ever-evolving connections - p. 195 16. Chapter 16. Transforming e-commerce with Graph Neural Networks: Enhancing personalization, security, and business growth - p. 215 17. Chapter 17. On ring domination in soft graphs - p. 225 18. Chapter 18. Graph data science: Applications and future - p. 227 19. Chapter 19. Verification of MPI programs via compilation into Petri nets - p. 245 20. Chapter 20. Demonstration and analysis of the performance of image caption generator: An effort for visually impaired candidates for smart cities 5.0 - p. 271 21. Index - p. 295
