Practical Graph Intelligence 1

Algorithms, Networks and Python Implementations

Pramod Singh Rathore, Abhishek Kumar, Priya Batta, Inam Ul Haq

• Publisher: Wiley-ISTE
• Year: 2026
• Print ISBN:
  9781836691402
• Edition: 1
• eBook ISBN:
  9781394479825
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

Practical Graph Intelligence 1 is positioned at the intersection of graph theory, network science and applied computing, offering a structured pathway for understanding and implementing graph-based solutions.

This book systematically develops core concepts in graph algorithms and network analysis, while emphasizing practical implementation using Python. It explores fundamental structures, traversal techniques, optimization strategies and real-world network modeling, enabling readers to translate theory into scalable applications. Through clear explanations and hands-on examples, the book supports learners in building analytical skills required for domains such as artificial intelligence (AI), data science, cybersecurity and social network analysis.

Designed for students, researchers and professionals, this book bridges the gap between mathematical foundations and computational practice, fostering the development of efficient and intelligent network-driven systems.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• Preface
• Introduction
• 1 Graph-Theoretic Foundations for Semantic Network Construction Through Transformer-based Feature Learning and Multi-Lingual Entity-Relation Graph Modeling
• 2 Sparse Tucker Decomposition with L1 Regularization: Matrix Completion in Tensor Networks
• 3 Principal Component Analysis (PCA) and t-SNE Combined with Autoencoder Embeddings for Graph-based Feature Engineering and Dimensionality Reduction
• 4 Instrumental Variable Regression and Double Machine Learning for Causal Effect Estimation in Graph-based Business Analytics
• 5 Gradient Boosting Machines (XGBoost, LightGBM) with Stacked Generalization for Multi-Task Learning in Graph-based Predictive Analytics
• 6 Spectral Graph Convolutional Networks for IoT Device Clustering and Anomalous Node Detection in Complex Network Topologies
• 7 Graph Neural Networks with Attention Mechanisms for Customer Segmentation and Churn Prediction in E-Commerce Platforms
• 8 Domain Adaptation via Maximum Mean Discrepancy (MMD) and Adversarial Domain Discriminators for Graph Neural Network Transfer Learning
• 9 Graph Intelligence-driven Reinforcement Learning Architecture for Modeling and Control of Microfluidic Transport Phenomena and Nonlinear Heat–Mass Coupled Nanofluid Flows
• 10 Graph Intelligence-based Numerical Solutions using Runge–Kutta and Caputo Fractional Derivatives for Nonlinear Biological Transport Equations
• 11 Mixed-Integer Linear Programming (MILP) with Column Generation for Vehicle Routing Problems with Time Windows Using Graph-based Route Optimization
• 12 Differential Evolution and Grey Wolf Optimization: Hybrid Metaheuristics for Constrained Non-Convex Problems in Graph-based Network Optimization
• 13 Stackelberg Game Theory with Nash Equilibrium Computation: Algorithmic Applications in Graph-based Resource Competition and Network Optimization
• 14 Graph Intelligence-enabled Quantum–Classical Hybrid Framework for Advanced Cybersecurity Threat Detection and Analytics
• 15 Graph Intelligence-driven DevOps Analytics: A Multi-Modal AI Platform for Predictive Performance Optimization
• 16 Graph Intelligence-driven Automated Software Deployment System with Integrated Testing Pipelines for Continuous Delivery
• List of Authors
• Index
• Other titles from iSTE in Computer Engineering
• End User License Agreement