Applied Graph Data Science

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

1st Edition - January 27, 2025
Editors: Pethuru Raj, Pushan Kumar Dutta, Peter Han Joo Chong, Houbing Herbert Song, Dmitry A. Zaitsev
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 6 5 4 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 6 5 5 - 0

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

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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.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1. Introduction to graph neural network: A systematic review of trends, methods, and applications
1 Introduction
2 Historical background of GNN
3 Architecture of GNN
4 Relationship between traditional graph embedding & GNN
4.1 Link prediction and graph categorization
4.2 Matching graphs and learning how to structure graphs
4.3 Automated machine learning and self-supervised learning
5 How GNN is better than CNN
6 Difficulties in graph learning process
7 Current trends in GNN
7.1 Graph Attention Networks
7.2 Graph representation learning
7.3 Transformer based GNN
7.4 Hierarchical GNNs
7.5 Classical Graph Generative Models
7.5.1 Erdos-Renyi model
7.5.2 Barabási Albert model
7.5.3 Watts Strogatz model
7.5.4 Forest Fire model
7.5.5 Stochastic Block Model
7.5.6 Exponential Random Graph Models
7.6 Graph pooling
8 Real world applications of GNN
8.1 Recommender systems
8.2 Computer vision
8.3 Natural Language Processing
8.4 Drug development
8.5 Social network analysis
8.6 Robotics industry
8.7 Fraud detection
9 Future directions in GNN
10 Conclusion
Chapter 2. Chronological reasoning in knowledge graphs using AI and ML: A novel framework
1 Introduction
1.1 Background and motivation
1.2 Significance of chronological reasoning in knowledge graphs
2 Related work
2.1 Overview of knowledge graphs and temporal reasoning
2.2 Previous approaches to AI and ML in knowledge graph analysis
3 Methodology
3.1 Data preprocessing and temporal annotation
3.2 Temporal embeddings using AI-enhanced ML techniques
3.3 ML-based chronological event detection
4 AI-enhanced temporal embeddings
4.1 Graph neural networks for temporal knowledge representation
4.2 Experimental results and comparison with baseline methods
5 ML-based chronological event detection
5.1 Feature engineering for event recognition
5.2 Machine learning models for event detection
5.3 Performance evaluation and case studies
6 Temporal link prediction using ML
6.1 Modeling temporal relationships for link prediction
6.2 Quantitative analysis and evaluation metrics
7 Applications and case studies
7.1 Predictive analysis of historical trends
7.2 Real-world applications of AI-driven chronological reasoning
7.2.1 Environmental monitoring
7.2.2 Healthcare and disease outbreak prediction
7.2.3 Manufacturing and quality control
7.2.4 Supply chain optimization
7.2.5 Customer behavior analysis
7.2.6 Personalized recommendations in E-commerce
7.2.7 Social network analysis
7.2.8 Urban planning and traffic management
8 Advantages and limitations of the proposed framework
8.1 Advantages
8.2 Limitations
9 Future directions
10 Conclusion
Chapter 3. Graph based approach on financial fraudulent detection and prediction
1 Introduction
2 Classification of frauds
3 Graph network and research direction
3.1 Simple versus bipartite
3.2 Homogeneous versus heterogeneous
3.3 Directed versus undirected
3.4 Static versus dynamic
3.5 Attributed versus unattributed
4 Different time-domain keeping track of network user activities over different timestamps
5 Multiplex behavior of users and network support
6 Eliminating human intervention in structural information extraction
7 Financial fraud and graphical neural network
7.1 Approaches of graphical neural network
7.2 Different types of graphs
8 Graph attention network
8.1 Attention score
9 Recurrent network
10 Conclusion and future direction
Chapter 4. The power of graph neural networks: From theory to application
1 Introduction
1.1 Importance of graphs in various domains
1.2 Challenges in processing graph-structured data
1.3 Motivation for using graph neural networks (GNNs)
2 Fundamentals of graph neural networks
2.1 Introduction to graph theory concepts
2.2 Overview of the design pipeline of GNNs
2.2.1 Data representation in graphs
2.2.2 Feature engineering for graph data
2.2.3 GNNs' pipeline design in a nutshell
2.3 Variants of graph neural networks
2.3.1 Graph convolutional networks
2.3.2 Graph recurrent neural networks
2.3.3 Graph attention networks
2.3.4 Graph generative networks
2.3.5 Spatial-temporal graph neural networks
2.3.6 Hybrid forms
2.4 GNNs taxonomy
3 Scalability and interpretability of graph neural networks
3.1 Scalability of GNNs
3.2 Interpretability of GNNs
4 Applications of graph neural networks
5 Open problems and future research directions
6 Conclusion
Chapter 5. Delineating graph neural networks (GNNs) and the real-world applications
1 Introduction
2 Briefing the distinctions of graphs
3 The emergence of graph data science
4 The emergence of graph neural networks (GNNs)
5 Graph analysis techniques
6 Demystifying deep neural networks on graph data
7 Graph neural networks (GNNs): The applications
8 A case study: Graph analytics for e-commerce
9 Types of graph neural networks (GNNs)
10 Graph convolutional network (GCN)
11 The challenges for graph neural networks (GNNs)
12 Conclusion
Chapter 6. Graph techniques for enhancing knowledge graph integration: A comprehensive study and applications
1 Introduction
1.1 Motivational example
2 Background
2.1 Semantic technologies, ontologies, knowledge graph
2.1.1 Ontologies
2.1.2 Knowledge Graph
3 Knowledge graph integration
3.1 Matching problem
3.2 Terminology used in knowledge graph matching
3.3 General process of knowledge graph matching
3.4 Heterogeneity and similarity measures
4 Graph matching techniques
5 Knowledge graph matching methods
5.1 Graph based techniques for ontology and knowledge graph matching
5.2 Taxonomy-based techniques
5.3 Graph neural networks base knowledge graph matching
5.4 Intersection of Knowledge Graph Integration, Knowledge Graph Matching, and Graph Matching
6 Case study: Enhancing semantic segmentation for remote sensing through knowledge graph matching
7 Application perspective of knowledge base integration
8 Conclusion
Chapter 7. Graphs, language models, and NLP: The future of search engines
1 Introduction
1.1 Limitations of traditional keyword-based approaches
1.2 Rationale for integration of graphs, language models, and NLP
1.2.1 Growing complexity of user queries
2 Graphs in search engines
2.1 Introduction to graph-based approaches
2.1.1 Definition of graphs in the context of search
2.1.2 Historical perspective on graphs in information retrieval
2.2 Advantages of graph-based representations
2.2.1 Enhanced contextual understanding
2.2.2 Improved relationship modeling
2.3 Challenges and considerations
2.3.1 Scalability issues
2.3.2 Ethical and privacy concerns
3 Language models and NLP in search
3.1 Language models in information retrieval
3.1.1 Introduction to language models
3.1.2 Applications in search engines
3.2 NLP techniques for enhanced search
3.2.1 Semantic search
3.2.2 Entity recognition and contextual understanding
3.3 Personalized search with language models
3.3.1 Tailoring results to user preferences
3.3.2 Dynamic language generation
4 Intersection of graphs, language models, and NLP
4.1 Integrating language models into graph structures
4.1.1 Creating interconnected nodes
4.1.2 Case studies demonstrating effectiveness
4.2 NLP techniques for graph-based search
4.2.1 Multilingual search capabilities
4.2.2 Addressing complex user queries
5 Case studies and applications
5.1 Google's knowledge graph
5.1.1 Overview and impact on search results
5.1.2 Integration with language models
5.1.3 Impact on user experience
5.1.4 Challenges and considerations
5.2 Open AI's GPT-4 in search engines
5.2.1 Utilizing advanced language models
5.2.2 Improving search relevance
6 Challenges and future directions
6.1 Ethical considerations in graph-based search
6.1.1 Responsible use of user data
6.1.2 Ensuring fair and unbiased results
6.2 Privacy concerns and user data protection
6.2.1 Balancing personalization with privacy
6.2.2 Implementing robust security measures
6.3 Scalability issues in large-scale graph-based systems
6.3.1 Handling massive datasets
6.3.2 Ensuring efficient processing
7 Conclusion
Chapter 8. Graph Data Science and ML techniques: Applications and future
1 Introduction
2 Literature review
3 Understanding Graph Data Science
3.1 Graph structures and representations
3.2 Graph algorithms and techniques
3.3 Machine learning in Graph Data Science
4 Applications of Graph Data Science
4.1 Social network analysis
4.2 Fraud detection
4.3 AI Powered Recommendation Systems
4.4 Knowledge graphs
5 Future directions of Graph Data Science
5.1 Healthcare
5.2 Cybersecurity
5.3 Artificial intelligence
6 Challenges and opportunities
6.1 Data quality and integration challenges
6.2 Scalability and efficiency challenges
6.3 Interpretability and explainability challenges
6.4 Ethical and privacy challenges
6.5 Domain-specific challenges
7 Conclusion
8 Future work
Chapter 9. Innovative feature engineering methods for graph data science
1 Introduction
2 Literature review
3 Fundamentals of graph structures and relationships
4 Node-level feature engineering: Unveiling insights with node embeddings
5 Capturing graph similarity: Exploring graph kernels for feature generation
6 Graph-specific attributes
7 Realizing analytical depth: Case studies of effective feature engineering
8 Social network analysis: Uncovering community dynamics
9 Recommendation systems: Enhancing personalized recommendations
10 Biological networks: Deciphering molecular interactions
11 Transportation networks: Optimizing route planning
12 Domain adaptability and iterative refinement in feature engineering
13 Exploratory analysis and data understanding
14 Future trends and innovations in graph data science feature engineering
15 Results
16 Conclusion
Chapter 10. Graph neural networks: Insight and applications
1 Introduction to graph neural network
1.1 Motivation behind graph neural network
1.2 Evolution and history of GNN
1.3 Limitations of traditional NN
2 Literature review on GNN
3 Architecture and components of GNN
4 Architectural variants
4.1 Spatial based GNN
4.2 Spectral based GNN
4.3 Attention based GNN
4.4 Graph recurrent neural network
4.5 Applications of GNN
5 Conclusion
Chapter 11. Graph-theoretic analysis for eco-efficient textile weaving patterns
1 Introduction
2 Graph representation of textile weaving patterns
3 Thread efficiency analysis
4 Material waste reduction
5 Environmental impact assessment
6 Future directions
Chapter 12. Quantum-assisted graph networks: Algorithmic innovations and optimization strategies for large scale social communities
1 Introduction
1.1 Challenges in Traditional Graph Networks
1.2 Motivation for quantum-assisted approaches
1.3 Scope and objectives
2 Classical algorithm in graph networks
2.1 Overview of existing algorithms
2.2 Limitations in scalability, accuracy, and efficiency
2.3 Suboptimal solutions
3 Quantum computing in graph networks
3.1 Introduction to quantum computing principles
3.2 Quantum algorithms for graph networks
3.3 Quantum Annealing
3.4 Quantum walk
3.5 Quantum Machine Learning
3.5.1 Quantum Graph Neural Networks
4 Design and optimization of quantum-assisted graph networks
4.1 Quantum framework for graph networks
4.1.1 Prediction network phase
4.1.2 Inverse training mechanism phase
4.2 Centrality analysis, link prediction, and anomaly detection
4.3 Randomized algorithms
5 Challenges and future directions
5.1 Other potential challenges
5.2 Future research directions
6 Conclusion and future scope
Chapter 13. Using physics-informed AI and graph-based quantum computing for natural catastrophic analysis: Future perspectives
1 Introduction
1.1 Objectives of the chapter
2 Related work
2.1 Training and optimization
2.2 Model evaluation and validation
2.3 Iterative refinement
2.4 Deployment and application
2.5 Continuous monitoring and updating
3 Methodology of the study
4 Physics-informed AI
5 GNN architecture design
6 Model training
7 Evaluation and validation
7.1 Integration approach
8 GNN architecture overview for calamity analysis
8.1 Integrating PINN with GNN for calamity analysis
9 Review of AI models
10 Conclusion and future implications
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
1 Introduction
1.1 Background of the chapter
1.2 Significance of disease screening, drug invention and additive manufacturing in modern society
1.3 Challenges of machine learning and deep learning
1.4 Objectives of the chapter
2 Knowledge graphs: Foundation and significance
3 Utilization of knowledge graphs: Healthcare and disease diagnosis
3.1 Knowledge graphs: Role in biomedical research
3.2 Obstacles in the representation of disease knowledge
3.3 Disease ontologies and standardization efforts
4 Knowledge graph for disease screening and cataloging
4.1 Application of machine learning and deep learning in healthcare
4.2 Image analysis for disease diagnosis
4.3 Predictive analytics for patient outcomes
4.4 Drug discovery and repurposing
4.5 Integration of machine learning and deep learning algorithms in knowledge graphs
5 Machine learning and deep learning algorithms for disease screening
5.1 Tools and approaches for drug invention
5.2 Drug discovery in the era of AI
5.3 High-throughput screening and virtual drug design
5.4 Drug repositioning and polypharmacology
6 Knowledge graphs for drug invention
6.1 Semantic drug discovery
6.2 Knowledge graphs in additive manufacturing
6.3 3D printing technologies and applications
6.4 Integrating knowledge graphs for additive manufacturing
6.5 Disease screening and drug invention: Knowledge graph
7 Scalability and standardization: Knowledge graph for disease screening and cataloging
8 Conclusion and future scope
Chapter 15. Analyzing social networks with dynamic graphs: Unravelling the ever-evolving connections
1 Introduction
2 Literature survey
3 The significance of time
3.1 Static graphs
3.2 Dynamic graphs
3.2.1 Evolution of a social network
4 Methodologies for dynamic graph analysis
4.1 Snapshot-based analysis
4.2.1 Snapshot attributes
4.2 Event-based analysis
4.2.1 Best event-based analytics platforms for actionable product insights
4.3 Continuous-time models
4.4 Community detection in dynamic networks
4.5 Streaming graph analysis
4.6 Machine learning for dynamic graphs
5 Challenges in dynamic graph analysis
6 Applications of dynamic graph analysis
7 Conclusion
Chapter 16. Transforming e-commerce with Graph Neural Networks: Enhancing personalization, security, and business growth
1 Introduction
2 Basics of Graph Neural Networks
2.1 Graph representation
2.1.1 Nodes and edges
2.1.2 Graph structure
2.1.3 Node features
2.1.4 Edge features
2.1.5 Graph labels
2.1.6 Graph types
2.1.7 Graph visualization
3 The potential of Graph Neural Networks in e-commerce
4 The literature review of Graph Neural Networks in context of e-commerce
5 GNNs work in the context of e-commerce
5.1 Data representation
5.2 Node embedding's
5.3 Message passing
5.4 Graph-level aggregation (optional)
5.5 Task-specific layers
5.6 Training
5.7 Inference
6 Algorithms used in GNNs in the context of e-commerce
7 Advantages and disadvantages of GNNs in the context of e-commerce
7.1 Advantages of GNNs in E-commerce
7.2 Disadvantages of GNNs in e-commerce
8 Conclusions
Chapter 17. On ring domination in soft graphs
1 Introduction
2 Preliminaries
3 Main results
Chapter 18. Graph data science: Applications and future
1 Introduction
1.1 What is graph data science?
1.2 Historical context of graph theory
1.3 Main contributions of the chapter
2 Overview of graphs
2.1 Basics of graph theory
2.1.1 Nodes/vertices
2.1.2 Edges
2.1.3 Type of graphs
3 Rise of graph data science
4 The graph data science and its applications
4.1 Graph applications in scientific studies
4.1.1 Use of graph in physics
4.1.2 Use of graph in chemistry
4.1.3 Use of graph in mathematics
4.1.4 Use of graph in biology
4.1.5 Graphs and electrical networks
4.2 Applications of graph theory computer sciences
4.2.1 Use of graph in website designing
4.2.2 Graph theory in network security
4.2.3 Graphical representation of communication network
4.2.4 Use of graph in mobile networks
4.2.5 Use of graph in computer vision
4.2.6 Graph databases
4.2.7 Use of graph in blockchain
4.2.8 Graph neural networks
4.2.9 Use of graph in algorithms
4.3 Applications of graph theory in social sciences
4.3.1 Use of graph in social media
4.3.2 Use of graph in linguistics
4.3.3 Use of graph in history
4.3.4 Use of graph in geography
4.4 Application of graphs in health sciences
5 Case studies
5.1 Graph analysis in the medical field
5.1.1 Finding more effective medicinal products
5.1.2 Enhancing the experience of the patient
5.2 Personalized marketing strategies and targeted advertising for effective guidance and promotion
5.3 The art of fraud detection
6 Challenges, barriers, and possible remedies for applications based on graph theory
7 The possibilities for further research
8 Conclusion
Chapter 19. Verification of MPI programs via compilation into Petri nets
1 Introduction
2 Overview of message passing interface and MPJ express
2.1 Basic operations of MPI
2.2 Compile and run MPJ express program
2.2.1 Deadlocks within MPI programs
3 Overview of MPI analyzers
4 Overview of Petri nets
5 Representation of MPI message exchange by Petri net
6 Compiling Java-MPI into Petri net
7 Case study: Analysis of Java-MPI programs via compilation into Petri nets
7.1 A simple example of a program without deadlocks
7.2 A simple example of a program with a deadlock
7.3 Example with a simple loop
7.4 A more complex example with a deadlock
7.5 Recommendations for resolving deadlocks
8 Prospective approaches to efficient analysis of big distributed software systems
8.1 Using structurally restricted subclasses of Petri nets
8.2 Avoiding explicit unfolding of loops with infinite Petri nets
9 Conclusions
Chapter 20. Demonstration and analysis of the performance of image caption generator: An effort for visually impaired candidates for smart cities 5.0
1 Motivation
2 Scope of the study
3 Topic organization
4 Introduction
4.1 Need of technology for blind people
4.2 Statistical annals for use of automated software across globe
4.3 Use of AI and deep learning in image caption generation
4.4 Different apps and tools for image caption generation
4.5 CPS and IoT-based application for caption generation
4.6 Cloud native architecture and image recognition
5 Literature review
6 Methodology and setup design of experiment
6.1 Selected dataset one sample image
6.2 Hardware requirement
6.3 Flow chart
6.4 ER diagram
6.5 Block diagram
6.6 Object oriented class diagram
7 Results and discussions
8 Novelties
9 Recommendations
10 Future research directions and limitations
10.1 Limitations
10.2 Future directions
11 Conclusions
Annexure
Key terms and definitions
Data sets
Additional readings
Index

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, India

Pushan Kumar Dutta

Dr. Pushan Kumar Dutta is an Assistant Professor Grade III at Amity University Kolkata, specializing in Electronics and Communication Engineering. He holds a Ph.D. in Electronics and Telecommunication Engineering from Jadavpur University and completed a Postdoctoral fellowship as an Erasmus Mundus Scholar at the University of Oradea. His research interests include data mining, AI, edge computing, and predictive analytics, focusing on applications in smart cities, healthcare, and sustainable development. Dr. Dutta has published over 114 articles in Scopus-indexed journals and has edited more than 30 books for prestigious publishers in 2023-2024. He is recognized as a reviewer for leading academic publishers and has received accolades such as the 'Mentor of Change' from NITI Aayog. Committed to innovative teaching, he authored "Innovative Digital Teaching and Learning for Professional Readiness" and holds two Indian patents. Dr. Dutta is a Threws Fellow member and a senior member of the Indian Institute of Engineering.

Affiliations and expertise

Assistant Professor, Grade III, School of Engineering and Technology, Amity University, Kolkata, India

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 Zealand

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, USA

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, France

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Applied Graph Data Science

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

Purchase options

Institutional subscription on ScienceDirect

Pethuru Raj

Pushan Kumar Dutta

Peter Han Joo Chong

Houbing Herbert Song

Dmitry A. Zaitsev