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Graph Theory with AI Applications
Everything is connected.
People, computers, businesses, cities, knowledge, and intelligent systems all exist within networks of relationships.
Understanding those relationships is the key to modern Artificial Intelligence.
The Graph Theory with AI Applications Complete Series (VOL-1 & VOL-2) takes readers from classical graph theory foundations to cutting-edge Graph Neural Networks and Graph AI.
Inside this bundle, you'll discover:
✓ Graph Theory Fundamentals
✓ Graph Traversal and Pathfinding Algorithms
✓ Shortest Path and Network Optimization
✓ Social Network Analysis
✓ Community Detection and Graph Mining
✓ Graph Representation Learning
✓ Graph Embeddings (DeepWalk, Node2Vec, LINE)
✓ Graph Neural Networks (GCN, GAT, GraphSAGE)
✓ Knowledge Graphs and Graph-Based NLP
✓ AI-Powered Recommendation Systems
✓ Cybersecurity and Fraud Detection
✓ Explainable Graph AI
✓ Distributed Graph Learning
✓ Graph Foundation Models
✓ Quantum Graph Neural Networks
Whether you are a student, researcher, educator, or AI professional, this bundle provides the mathematical foundations and modern AI techniques required to understand how intelligent systems learn from connected data.
Learn Graph Theory.
Master Graph Neural Networks.
Build the future of connected intelligence.
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$29.00
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The following 2 books are included in this bundle...
These books have a total suggested price of $39.98. Get them now for only $29.00!
About
About the Bundle
Graph Theory with AI Applications Complete Series
Foundations, Algorithms, Graph Neural Networks, and Modern Graph IntelligenceThe future of Artificial Intelligence is connected.
From social media networks and recommendation engines to autonomous vehicles, cybersecurity platforms, biological systems, financial transaction networks, and large-scale intelligent infrastructures, modern AI increasingly depends on understanding relationships rather than isolated data points.
Graphs provide the mathematical language for modeling these relationships.
Artificial Intelligence provides the ability to learn from them.
The Graph Theory with AI Applications Complete Series (VOL-1 & VOL-2) offers a comprehensive journey from classical graph theory to cutting-edge Graph Neural Networks (GNNs), graph embeddings, graph representation learning, and the emerging world of Graph AI.
This two-volume series bridges mathematical foundations, algorithmic techniques, and modern artificial intelligence applications, providing readers with a complete framework for understanding how machines learn from connected data.
Volume I: Foundations and Classical Graph Algorithms
The first volume establishes the mathematical and algorithmic foundations of graph theory and demonstrates how graph structures power real-world intelligent systems.
Readers will explore:
✓ Graph Fundamentals and Terminology
✓ Directed, Undirected, Weighted, and Dynamic Graphs
✓ Graph Representations and Matrix Models
✓ Graph Traversal Algorithms (BFS, DFS)
✓ Topological Sorting and Connectivity Analysis
✓ Shortest Path Algorithms
✓ Dijkstra, Bellman-Ford, Floyd-Warshall, and Johnson Algorithms
✓ AI Pathfinding Techniques including A*
✓ Minimum Spanning Trees and Network Optimization
✓ Network Flow and Resource Allocation
✓ Social Network Analysis
✓ Centrality Measures and Influence Analysis
✓ Community Detection Algorithms
✓ Link Prediction and Graph Mining
✓ Real-World Applications in Search, Navigation, Robotics, and Recommendation Systems
Volume I provides the strong theoretical foundation necessary for understanding modern graph-based artificial intelligence systems.
Volume II: Graph Neural Networks and Graph AI
Building upon the foundations of Volume I, the second volume explores how artificial intelligence learns directly from graph-structured data.
Readers will discover:
✓ Graph Representation Learning
✓ Machine Learning on Graphs
✓ Graph Neural Networks (GNNs)
✓ Graph Convolutional Networks (GCNs)
✓ Graph Attention Networks (GATs)
✓ GraphSAGE and Message Passing Neural Networks
✓ Graph Embeddings: DeepWalk, Node2Vec, LINE
✓ Graph Autoencoders
✓ Contrastive Graph Learning
✓ Knowledge Graphs and Graph-Based NLP
✓ Graph AI for Recommendation Systems
✓ Social Network Intelligence
✓ Cybersecurity Applications of Graph AI
✓ Robotics and Autonomous Systems
✓ Explainable Graph Neural Networks
✓ Fairness, Bias, and Privacy in Graph Learning
✓ Distributed Graph Processing
✓ Graph Foundation Models
✓ Neural-Symbolic Graph Reasoning
✓ Quantum Graph Neural Networks
Volume II provides readers with the advanced techniques and research insights driving the next generation of intelligent graph-based systems.
Why This Bundle Is Unique
Most graph theory books focus solely on mathematical foundations.
Most AI books focus on neural networks without explaining the graph structures that underpin many modern intelligent systems.
This bundle uniquely combines both worlds.
Readers will learn:
• How graph algorithms power search engines and navigation systems.
• How social networks are analyzed using graph structures.
• How recommendation systems learn user preferences.
• How Graph Neural Networks learn from connected data.
• How graph embeddings transform networks into machine-learning-ready representations.
• How Graph AI powers cybersecurity, robotics, NLP, healthcare, finance, and intelligent infrastructures.
• How future Graph Foundation Models may reshape artificial intelligence.
Who Should Read This Bundle?
This series is ideal for:
• Undergraduate and Postgraduate Students
• Computer Science and AI Researchers
• Data Scientists and Machine Learning Engineers
• Graph Neural Network Researchers
• Network Scientists
• Cybersecurity Professionals
• NLP and Knowledge Graph Practitioners
• Robotics and Autonomous Systems Engineers
• Faculty Members and Educators
• GATE, UGC-NET, PhD, and Research Scholars
What You Will Learn
By completing this bundle, readers will be able to:
✓ Understand graph structures and graph algorithms.
✓ Apply shortest path, optimization, and network analysis techniques.
✓ Perform community detection and graph mining.
✓ Build graph representations and embeddings.
✓ Design and train Graph Neural Networks.
✓ Apply Graph AI in recommendation systems, cybersecurity, NLP, and robotics.
✓ Analyze fairness, privacy, and explainability in graph learning systems.
✓ Explore emerging technologies such as Graph Foundation Models and Quantum GNNs.
Build Intelligence Through Connections
Artificial Intelligence is increasingly moving beyond isolated data points toward learning from relationships, interactions, and complex networks.
Graphs are becoming the foundation of this transformation.
The Graph Theory with AI Applications Complete Series provides the mathematical foundations, algorithmic understanding, and modern AI techniques necessary to master Graph Intelligence and participate in one of the fastest-growing areas of artificial intelligence research and development.
Learn the science of connected systems.
Master the algorithms of intelligent networks.
Build the future of Graph AI.
Books
About the Books
Graph Theory with AI Applications VOL-1
Algorithms and Modern Neural Approaches
Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-1) is a comprehensive academic and professional guide that explores the powerful intersection of Graph Theory and Artificial Intelligence. In today's data-driven world, many real-world systems—from social networks and communication infrastructures to recommendation engines and autonomous systems—are naturally represented as graphs. Understanding graph structures and algorithms has therefore become essential for students, researchers, and AI practitioners.
This volume provides a strong foundation in classical graph theory while gradually connecting these concepts to modern AI applications. Readers begin with the fundamentals of graph structures, graph representations, traversal algorithms, shortest-path techniques, spanning trees, network flows, and graph optimization methods. The book then extends these foundations into social network analysis, community detection, centrality measures, link prediction, and graph mining.
Unlike traditional graph theory textbooks, this work emphasizes practical relevance and computational applications. Each chapter demonstrates how graph-based methods are used in robotics, navigation systems, recommendation engines, fraud detection, search algorithms, and intelligent decision-making systems.
Written in a structured and learner-friendly manner, the book combines mathematical rigor, algorithmic understanding, and application-oriented insights. It serves as an ideal resource for undergraduate and postgraduate students, AI researchers, data scientists, software engineers, and professionals interested in network analytics and intelligent systems.
As the first volume of a two-volume series, this book establishes the theoretical and algorithmic foundations required for understanding advanced Graph Neural Networks (GNNs), graph embeddings, and graph-based machine learning techniques that are covered in Volume II.
Whether you are preparing for academic examinations, conducting research, or building intelligent graph-based applications, this book provides the knowledge and skills necessary to navigate the rapidly evolving world of Graph AI.
Graph Theory with AI Applications VOL-2
Algorithms and Modern Neural Approaches
Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) continues the journey from classical graph theory into the rapidly evolving world of Graph Artificial Intelligence (Graph AI). While Volume I established the mathematical foundations, graph algorithms, social network analysis, and graph mining techniques, this volume focuses on modern graph representation learning, Graph Neural Networks (GNNs), graph embeddings, explainable graph AI, and emerging research frontiers.
As modern data increasingly takes the form of interconnected networks rather than traditional tabular structures, graph-based machine learning has emerged as one of the most important fields in artificial intelligence. Social networks, recommendation systems, knowledge graphs, cybersecurity infrastructures, molecular structures, financial transaction networks, and autonomous systems all generate graph-structured data that requires specialized learning techniques.
This book provides a comprehensive exploration of Graph Neural Networks, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), GraphSAGE, Message Passing Neural Networks (MPNNs), Relational GNNs, Temporal GNNs, and large-scale industrial graph learning systems.
Readers will learn how graph embeddings such as DeepWalk, Node2Vec, LINE, and Graph Autoencoders enable machines to learn meaningful representations from complex networks. The book further demonstrates how these techniques power recommendation engines, social network analysis, cybersecurity systems, knowledge graphs, natural language processing applications, robotics, and intelligent decision-making systems.
A distinguishing feature of this volume is its strong emphasis on modern AI applications and future research directions. Topics such as explainable graph learning, fairness in graph AI, privacy-preserving graph analytics, distributed graph processing, graph foundation models, neural-symbolic graph reasoning, and quantum graph neural networks are presented in a structured and accessible manner.
Designed for students, researchers, educators, and industry professionals, this book serves as both an academic textbook and a practical reference guide for anyone seeking expertise in graph-based artificial intelligence and modern network learning systems.
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