Applied Graph Data Science
Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
- 1st Edition - January 27, 2025
- Latest edition
- Editors: Pethuru Raj, Pushan Kumar Dutta, Peter Han Joo Chong, Houbing Herbert Song, Dmitry A. Zaitsev
- Language: English
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the ap… Read more
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
Computer Science researchers, data science researchers, and data analysis researchers in academia and industry. The primary audience also includes researchers and professionals in the fields of mathematics, IT, physics, engineering, biomedicine, AI, ML, biology, healthcare, sociology, economics, IoT, cybersecurity, logistics, and transportation.
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
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
- Edition: 1
- Latest edition
- Published: January 27, 2025
- Language: English
PR
Pethuru Raj
Pethuru Raj PhD works as chief architect and vice president of site reliability engineering (SRE) division of Reliance Jio Infocomm. Ltd. Bangalore. Previously he worked as a cloud infrastructure architect in the IBM Global Cloud Center of Excellence (CoE), Bangalore. He worked as a TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division and as a lead architect in the corporate research (CR) division of Robert Bosch, India. He has gained more than 18 years of IT industry experience.
He finished the CSIR-sponsored PhD degree in Anna University, Chennai and continued the UGC-sponsored postdoctoral research in the department of Computer Science and Automation, Indian Institute of Science, Bangalore. Thereafter, he was granted a couple of international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. He has authored and edited 18 books thus far and he focuses on some of the emerging technologies such as Containerized Clouds; Big, Fast, and Streaming Data Analytics; Microservices architecture (MSA); Machine and Deep Learning Algorithms; Blockchain Technology; The Internet of Things; and Edge Computing. He has published more than 30 research papers in peer-reviewed journals such as IEEE, ACM, Springer-Verlag, Inderscience, etc.
Affiliations and expertise
Reliance Jio Platforms Ltd.. (RJIL), Bangalore, IndiaPD
Pushan Kumar Dutta
Dr. Pushan Kumar Dutta is an Associate Professor at Amity University Kolkata who has carved a distinctive niche in academia with over 12 years of academic experience. He earned his Ph.D. from Jadavpur University in 2015, followed by postdoctoral work as an Erasmus Mundus Scholar at the University of Oradea in Romania in 2016, which laid the foundation for a career drive for a position in institution of academic excellence. At the forefront of his contributions is an impressive editorial portfolio. Dr. Dutta has meticulously curated and edited over 60 books in the past couple of years for globally recognized publishers such as Springer, Elsevier, IET, Taylor & Francis, Cambridge Scholar Publishers, Degruyter, and IGI Global. His role as the manager of the "Sustainable Industrial Engineering Systems" book series for CRC Press further underscores his influence in shaping scholarly discourse on sustainability and engineering innovation. His editorial expertise spans rapidly evolving domains including artificial intelligence in healthcare, Industry 5.0 technologies, quantum computing applications,aviation and digital transformation strategies. A dedicated mentor and educator, Dr. Dutta has consistently empowered his students to excel and innovate, guiding them to garner international recognition through competitions like the Johnson Control International Whitepaper contest and Ideathon 5.0. His contributions to nurturing young talent have been acknowledged through accolades such as the "Mentor of Change" award by NITI Aayog, along with the prestigious Young Faculty Awards in 2018 and 2024. Complementing his teaching and editorial endeavors is an extensive research portfolio that includes over 200 Scopus-indexed publications, two pioneering Indian patents, and an Indian copyright. His professional recognitions extend to prestigious memberships and fellowships, including Senior Membership at the Indian Institute of Engineering, a Threws Fellowship, and a Fellowship at the EnTech Foundation. Dr. Dutta’s academic journey also includes significant teaching stints as an Assistant Professor at RGMCET in Nandyal, Andhra Pradesh, and as a lecturer for the Marine Electro Technology course at DG Shipping Taratala.
Affiliations and expertise
Assistant Professor, Grade III, School of Engineering and Technology, Amity University, Kolkata, IndiaPC
Peter Han Joo Chong
Professor Peter Han Joo Chong is the Associate Head of School (Research) at the School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand. Between 2016 and 2021, he was the Head of Department of Electrical and Electronic Engineering at AUT. He received the B.Eng. (with distinction) in Electrical Engineering from the Technical University of Nova Scotia, Canada, in 1993, and the M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of British Columbia, Canada, in 1996 and 2000, respectively. He has visited Tohoku University, Japan, as a Visiting Scientist in 2010 and Chinese University of Hong Kong (CUHK), Hong Kong, between 2011 and 2012. He is currently an Adjunct Professor at the Department of Information Engineering, CUHK. He is an Honorary Professor at Amity University, India. He is a Fellow of the Institution of Engineering and Technology (FIET), UK. Prof. Chong is listed in the World's Top 2% Scientists published by Stanford University in 2022. Before joining AUT in 2016, Professor Chong was an Associate Professor (tenured) from July 2009 to April 2016 and Assistant Professor from May 2002 to June 2009 at the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore. Between 2011 and 2013, he was the Assistant Head of Division of Communication Engineering. Between 2013 and 2016, he was the Director of Infinitus, Centre for Infocomm Technology. He was the recipient of ‘EEE Teaching Excellence Award’ and ‘Nanyang Award Excellence in Teaching’ in 2010, and ‘Nanyang Education Award (College)’ in 2015. In 2015, he became a Fellow of the Teaching Excellence Academy in NTU. From February 2001 to May 2002, he was with the Radio Communications Laboratory at Nokia Research Center, Finland. Between July 2000 and January 2001, he worked in the Advanced Networks Division at Agilent Technologies Canada Inc., Canada. He co-founded P2 Wireless Technology in Hong Kong in 2009 and Zyetric Technologies in Hong Kong, New Zealand and US in 2017. His current research projects focus on machine learning techniques applied to software defined vehicular networks. He has been developing techniques of deep reinforcement learning (DRL)-based resource management for future 5G-V2X networks. His research interests are in the areas of wireless/mobile communications systems including radio resource management, multiple access, MANETs/VANETs, green radio networks and 5G-V2X networks. He has published over 300 journal and conference papers, 1 edited book and 13 book chapters in the relevant areas.
Affiliations and expertise
Auckland University of Technology, New ZealandHS
Houbing Herbert Song
Houbing Song, Security and Optimization for Networked Globe Laboratory, University of Maryland, Baltimore County (UMBC), Baltimore, USA. His research interests include cyber-physical systems, cybersecurity and privacy, IoT, big data analytics, connected vehicles, smart health, wireless communications, and networking. Dr. Song has edited and authored several books in the field, including Cyber-Physical Systems: Foundations, Principles and Applications.
Affiliations and expertise
University of Maryland, Baltimore County (UMBC), Baltimore, USADZ
Dmitry A. Zaitsev
Dmitry A. Zaitsev received the Eng. degree in applied mathematics from Donetsk Polytechnic Institute, Donetsk, Ukraine, in 1986, the Ph.D. degree in automated control from the Kiev Institute of Cybernetics, Kiev, Ukraine, in 1991, and the D.Sc. degree in telecommunications from the Odessa National Academy of Telecommunications, Odessa, Ukraine, in 2006. He is a professor of Computer Science Department, University of Information Technology and Management in Rzeszow, senior member of the ACM and IEEE, recently visiting professor to Université Côte d’Azur, France. In 2017, he was a visiting professor to The University of Tennessee Knoxville, USA on a Fulbright scholarship, working in the Innovative Computing Laboratory headed by Jack Dongarra. As a result, a joint paper was published and software ParAd issued. Dmitry A. Zaitsev developed: theory of linear system clans; small universal Petri and Sleptsov nets in explicit form; generalized neighbourhood for cellular automata; theory of infinite Petri nets; Sleptsov net computing; equivalent transformations of timed Petri nets, algorithm for fuzzy logic function synthesis. He designed the Opera-Topaz system for production control, models of protocols and networking technologies TCP, BGP, IOTP, MPLS, Bluetooth, PBB, offered and implemented in the Linux kernel a new stack of networking protocols E6.
Affiliations and expertise
Université Côte d’Azur, FranceRead Applied Graph Data Science on ScienceDirect