“Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases” offers a comprehensive exploration into how graph data science enhances data science applications. This book intricately details the emerging field of graph data science, shedding light on its practical research implications and real-world applications. Readers can expect to gain deep insights into graph data science, including graph analytics, algorithms, databases, platforms, and diverse use cases across various research domains.
Additionally, the book illustrates how graphs function as a programming language, particularly highlighting Sleptsov Net Computing as a fully graphical concurrent processing language tailored for supercomputers. As graph data science evolves, it becomes an expressive means of representing diverse data types and their intricate relationships. This includes valuable tools such as graph query languages, databases, algorithms, and platforms. Consequently, powerful analytical methods and machine learning/deep learning (ML/DL) algorithms are rapidly advancing to derive insights from graph data.
This leads to the emergence of innovative use cases across scientific research and various industry sectors that leverage graph data representation and manipulation. The book provides effective solutions to complex business and scientific challenges through graph data analysis, equipping readers with essential conceptual foundations and technical techniques for harnessing these potent strategies.
Key highlights of the book include:
– In-depth coverage of the burgeoning field of graph data science and its practical applications.
– Practical guidance on tackling complicated data analysis challenges using graph data science, focusing on advanced analysis techniques such as graph neural networks (GNNs), machine learning, algorithms, graph databases, and query languages.
– Examination of advanced graph models including bipartite directed graphs, dynamic Petri, and Sleptsov nets, as well as the use of graphs as programming languages.
– A holistic presentation of core tools and techniques alongside foundational concepts in graph theory, encompassing mathematical theories, research methods, and graph analytics.
This book is a must-read for anyone looking to unlock the potential of graph data science in their data analysis endeavors.
