Applied Graph Data Science

Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases

Pethuru Raj, Pushan Kumar Dutta, Peter Han Joo Chong, Houbing Herbert Song, Dmitry A. Zaitsev

• Publisher: Morgan Kaufmann
• Year: 2025
• eBook ISBN: 9780443296550
• Edition: 0
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

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. - Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applications - Gives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languages - Covers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languages - Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

Table of contents

• Front Cover
• Applied Graph Data Science
• Applied Graph Data Science
• Copyright
• Contents
• Contributors
• 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 co ...
• 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 catalog ...
• 15 - Analyzing social networks with dynamic graphs: Unravelling the ever-evolving connections
• 16 - Transforming e-commerce with Graph Neural Networks: Enhancing personalization, security, and business growth
• 17 - On ring 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 cand ...
• Index
• Back Cover