Pramod Singh Rathore is an Assistant Professor at Manipal University Jaipur, India. His expertise includes NS2, networks, data mining and DBMS.Abhishek Kumar is a Professor at Chandigarh University, India. His expertise includes AI, renewable energy and image processing.Priya Batta is an Associate Professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. Her expertise includes AI, blockchain and IoT.Inam Ul Haq is an Assistant Professor at the School of Engineering and Technology (SET), CGC University Mohali, Punjab, India. His expertise includes AI, machine learning and quantum computing.

• Preface xvPramod Singh RATHORE, Abhishek KUMAR, Priya BATTA and Inam UL HAQIntroduction xviiPramod Singh RATHORE, Abhishek KUMAR, Priya BATTA and Inam UL HAQChapter 1. Graph-Theoretic Foundations for Semantic Network Construction Through Transformer-based Feature Learning and Multi-Lingual Entity-Relation Graph Modeling 1Vanampalli MOUNIKA, Kotla Lakshmi SRAVANTHI, Kumkuma PRANEETHA, Nadipi SAMSKRUTHI, Kodamanchili VARSHITHA and Karukula MANISHA1.1. Introduction 21.2. Literature review 31.3. Model architecture and methodology. 51.4. Results and performance analysis 91.5. Conclusion 141.6. References 15Chapter 2. Sparse Tucker Decomposition with L1 Regularization: Matrix Completion in Tensor Networks 17T. SRIKANTH, E. CHANDANA, A. MANJUSHA, B. PRANITHA, D. VARSHITHA and Lokam HARIKA2.1. Introduction 172.2. Literature review 192.3. Methodology 202.4. Results 252.5. Conclusion 282.6. References 29Chapter 3. Principal Component Analysis (PCA) and t-SNE Combined with Autoencoder Embeddings for Graph-based Feature Engineering and Dimensionality Reduction 31P.V.S. SWOJANYA, Lokam HARIKA, Jangannagari DIVYA, Manyada AKANKSHA, Mandhadi ASHWINI and Karri LAHARI3.1. Introduction 323.2. Literature review 333.3. Design of framework and methodology 343.4. Findings and performance evaluation 383.5. Discussion 423.6. Conclusion 433.7. References 44Contents viiChapter 4. Instrumental Variable Regression and Double Machine Learning for Causal Effect Estimation in Graph-based Business Analytics 47P.V.S. SWOJANYA, Kuthuru VARALAXMI, Gurka RUPA SRI, Kodiripaka AKSHAYA, J. Salony PAWAR and Mallisetti Sai NIKHITHA4.1. Introduction 484.2. Literature review 494.3. Methodology and causal framework 504.4. Findings and performance studies 534.5. Discussion 594.6. Conclusion 594.7. References 60Chapter 5. Gradient Boosting Machines (XGBoost, LightGBM) with Stacked Generalization for Multi-Task Learning in Graph-based Predictive Analytics 63L. Srinivasa REDDY, Madhagani Siri DHATHRIKA, Guguloth SINDHUKEERTHANA, Kadari REKHA, K. BHARGAVI and G. SONI5.1. Introduction 645.2. Related work 655.3. Methodology 665.4. Results 715.5. Conclusion 755.6. References 76Chapter 6. Spectral Graph Convolutional Networks for IoT Device Clustering and Anomalous Node Detection in Complex Network Topologies 79S. PRIYADHARSINI, R. VENKATESH, T. SIVAPRAKASAM, Iyappan MURUGESAN, K. SIVAPRASATH and Jegan CHELLAKANNU6.1. Introduction 796.2. Literature review 806.3. Methodology 836.4. Experimental setup 856.5. Results 866.6. Discussion 906.7. Conclusion 926.8. References 92Chapter 7. Graph Neural Networks with Attention Mechanisms for Customer Segmentation and Churn Prediction in E-Commerce Platforms 95Korra SRINIVAS, Barla MEGHANA, Sumana Sri AASHILY, Bandla SIRI, A. SRIVIDYA and E. SAHITHI7.1. Introduction 957.2. Literature review 977.3. Methodology 997.4. Results 1047.5. Conclusion 1077.6. References 108Chapter 8. Domain Adaptation via Maximum Mean Discrepancy (MMD) and Adversarial Domain Discriminators for Graph Neural Network Transfer Learning 111T. KAVITHA, Kurri SRAVANI, Mandha NANDHINI, Kothapally SHIRISHA REDDY, Muddam AKHILA and Jilla TARUNI8.1. Introduction 1128.2. Related work 1138.3. Methodology 1148.4. Results 1198.5. Conclusion 1238.6. References 124Chapter 9. Graph Intelligence-driven Reinforcement Learning Architecture for Modeling and Control of Microfluidic Transport Phenomena and Nonlinear Heat–Mass Coupled Nanofluid Flows 127L. MANJULA, K. RAMACHANDRAN, T.R.K. KUMAR, S. Leoni SHARMILA, R. BALAPRIYA and R. VANAJA9.1. Introduction 1289.2. Literature review 129x Practical Graph Intelligence 19.3. Methodology 1319.4. Results 1369.5. Conclusion 1409.6. References 140Chapter 10. Graph Intelligence-based Numerical Solutions using Runge–Kutta and Caputo Fractional Derivatives for Nonlinear Biological Transport Equations 143T. SRIKANTH, Alla ASRITHA, A. VAISHNAVI, Batchu MANASWI, B. SATHVIKA and Chandragiri SUSHMA10.1. Introduction 14310.2. Literature review 14510.3. Methodology 14710.4. Results 15310.5. Conclusion 15610.6. References 156Chapter 11. Mixed-Integer Linear Programming (MILP) with Column Generation for Vehicle Routing Problems with Time Windows Using Graph-based Route Optimization 159M. CHANDRARAO, K. JAGRUTHI, Kandlapally USHA SRI, Ledalla HIMAVARSHA, K.H. SHREYA and K. SAHITHI11.1. Introduction 16011.2. Literature review 16011.3. Problem formulation and methodology 16211.4. Findings and dynamic reviews 16611.5. Discussion 17011.6. Conclusion 17111.7. References 172Chapter 12. Differential Evolution and Grey Wolf Optimization: Hybrid Metaheuristics for Constrained Non-Convex Problems in Graph-based Network Optimization 175Anil JAWALKAR, Gunnam HARSHINI, Macharla SHIVANI, Mitnala SHIVANI, K.B. RENUKA and Jupally SAMHITHA12.1. Introduction 17612.2. Literature review 17612.3. Methodology 17812.4. Findings and analysis of performance 18212.5. Conclusion 18712.6. References 188xii Practical Graph Intelligence 1Chapter 13. Stackelberg Game Theory with Nash Equilibrium Computation: Algorithmic Applications in Graph-based Resource Competition and Network Optimization 191Ch. Sandeep REDDY, Guntuka Kavya KRUTHIKA, Neeraja HANNALA, Jadhav KALPANA, K. MAHITHA SRI SATWIKA and K. VIDYADHARI13.1. Introduction 19113.2. Literature review 19213.3. Methodology 19413.4. Results 19813.5. Discussion 20213.6. Conclusion 20313.7. References 204Chapter 14. Graph Intelligence-enabled Quantum–Classical Hybrid Framework for Advanced Cybersecurity Threat Detection and Analytics 207A. AGALYA and Priyadarsini K.14.1. Introduction 20814.2. Related work 20914.3. Quantum–classical threat detection framework 21114.4. Implementation and experimental design 21314.5. Discussion and analysis 21814.6. Conclusion 22014.7. References 221Chapter 15. Graph Intelligence-driven DevOps Analytics: A Multi-Modal AI Platform for Predictive Performance Optimization 223Sanke Stephen BABU, Yatham Chandra PRAKASH REDDY, Maguluri Durga SAI SRI, Vemareddy LOKESH and Shaik Jilani BASHA15.1. Introduction 22415.2. Related work 22515.3. System architecture and design 22715.4. Implementation and evaluation 22915.5. Discussion and future work 23315.6. Conclusion 23415.7. References 235Chapter 16. Graph Intelligence-driven Automated Software Deployment System with Integrated Testing Pipelines for Continuous Delivery 237Sree Vardhan SAI KURRA, Addanki Lakshmi SAI ROHITH, Rapolu Chandra MAHESH BABU, Manepalli KAVYA SRI and B. Prameela RANI16.1. Introduction 23816.2. Literature review 24016.3. Methodology 24116.4. Results and discussion 24316.5. Conclusion 24516.6. References 246List of Authors 249Index 259