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Preface xv
Pramod Singh RATHORE, Abhishek KUMAR, Priya BATTA and Inam UL HAQ

Introduction xvii
Pramod Singh RATHORE, Abhishek KUMAR, Priya BATTA and Inam UL HAQ

Chapter 1. Graph-Theoretic Foundations for Semantic Network Construction Through Transformer-based Feature Learning and Multi-Lingual Entity-Relation Graph Modeling 1
Vanampalli MOUNIKA, Kotla Lakshmi SRAVANTHI, Kumkuma PRANEETHA, Nadipi SAMSKRUTHI, Kodamanchili VARSHITHA and Karukula MANISHA

1.1. Introduction 2
1.2. Literature review 3
1.3. Model architecture and methodology. 5
1.4. Results and performance analysis 9
1.5. Conclusion 14
1.6. References 15

Chapter 2. Sparse Tucker Decomposition with L1 Regularization: Matrix Completion in Tensor Networks 17
T. SRIKANTH, E. CHANDANA, A. MANJUSHA, B. PRANITHA, D. VARSHITHA and Lokam HARIKA

2.1. Introduction 17
2.2. Literature review 19
2.3. Methodology 20
2.4. Results 25
2.5. Conclusion 28
2.6. References 29

Chapter 3. Principal Component Analysis (PCA) and t-SNE Combined with Autoencoder Embeddings for Graph-based Feature Engineering and Dimensionality Reduction 31
P.V.S. SWOJANYA, Lokam HARIKA, Jangannagari DIVYA, Manyada AKANKSHA, Mandhadi ASHWINI and Karri LAHARI

3.1. Introduction 32
3.2. Literature review 33
3.3. Design of framework and methodology 34
3.4. Findings and performance evaluation 38
3.5. Discussion 42
3.6. Conclusion 43
3.7. References 44Contents vii

Chapter 4. Instrumental Variable Regression and Double Machine Learning for Causal Effect Estimation in Graph-based Business Analytics 47
P.V.S. SWOJANYA, Kuthuru VARALAXMI, Gurka RUPA SRI, Kodiripaka AKSHAYA, J. Salony PAWAR and Mallisetti Sai NIKHITHA

4.1. Introduction 48
4.2. Literature review 49
4.3. Methodology and causal framework 50
4.4. Findings and performance studies 53
4.5. Discussion 59
4.6. Conclusion 59
4.7. References 60

Chapter 5. Gradient Boosting Machines (XGBoost, LightGBM) with Stacked Generalization for Multi-Task Learning in Graph-based Predictive Analytics 63
L. Srinivasa REDDY, Madhagani Siri DHATHRIKA, Guguloth SINDHUKEERTHANA, Kadari REKHA, K. BHARGAVI and G. SONI

5.1. Introduction 64
5.2. Related work 65
5.3. Methodology 66
5.4. Results 71
5.5. Conclusion 75
5.6. References 76

Chapter 6. Spectral Graph Convolutional Networks for IoT Device Clustering and Anomalous Node Detection in Complex Network Topologies 79
S. PRIYADHARSINI, R. VENKATESH, T. SIVAPRAKASAM, Iyappan MURUGESAN, K. SIVAPRASATH and Jegan CHELLAKANNU

6.1. Introduction 79
6.2. Literature review 80
6.3. Methodology 83
6.4. Experimental setup 85
6.5. Results 86
6.6. Discussion 90
6.7. Conclusion 92
6.8. References 92

Chapter 7. Graph Neural Networks with Attention Mechanisms for Customer Segmentation and Churn Prediction in E-Commerce Platforms 95
Korra SRINIVAS, Barla MEGHANA, Sumana Sri AASHILY, Bandla SIRI, A. SRIVIDYA and E. SAHITHI

7.1. Introduction 95
7.2. Literature review 97
7.3. Methodology 99
7.4. Results 104
7.5. Conclusion 107
7.6. References 108

Chapter 8. Domain Adaptation via Maximum Mean Discrepancy (MMD) and Adversarial Domain Discriminators for Graph Neural Network Transfer Learning 111
T. KAVITHA, Kurri SRAVANI, Mandha NANDHINI, Kothapally SHIRISHA REDDY, Muddam AKHILA and Jilla TARUNI

8.1. Introduction 112
8.2. Related work 113
8.3. Methodology 114
8.4. Results 119
8.5. Conclusion 123
8.6. References 124

Chapter 9. Graph Intelligence-driven Reinforcement Learning Architecture for Modeling and Control of Microfluidic Transport Phenomena and Nonlinear Heat–Mass Coupled Nanofluid Flows 127
L. MANJULA, K. RAMACHANDRAN, T.R.K. KUMAR, S. Leoni SHARMILA, R. BALAPRIYA and R. VANAJA

9.1. Introduction 128
9.2. Literature review 129x Practical Graph Intelligence 1
9.3. Methodology 131
9.4. Results 136
9.5. Conclusion 140
9.6. References 140

Chapter 10. Graph Intelligence-based Numerical Solutions using Runge–Kutta and Caputo Fractional Derivatives for Nonlinear Biological Transport Equations 143
T. SRIKANTH, Alla ASRITHA, A. VAISHNAVI, Batchu MANASWI, B. SATHVIKA and Chandragiri SUSHMA

10.1. Introduction 143
10.2. Literature review 145
10.3. Methodology 147
10.4. Results 153
10.5. Conclusion 156
10.6. References 156

Chapter 11. Mixed-Integer Linear Programming (MILP) with Column Generation for Vehicle Routing Problems with Time Windows Using Graph-based Route Optimization 159
M. CHANDRARAO, K. JAGRUTHI, Kandlapally USHA SRI, Ledalla HIMAVARSHA, K.H. SHREYA and K. SAHITHI

11.1. Introduction 160
11.2. Literature review 160
11.3. Problem formulation and methodology 162
11.4. Findings and dynamic reviews 166
11.5. Discussion 170
11.6. Conclusion 171
11.7. References 172

Chapter 12. Differential Evolution and Grey Wolf Optimization: Hybrid Metaheuristics for Constrained Non-Convex Problems in Graph-based Network Optimization 175
Anil JAWALKAR, Gunnam HARSHINI, Macharla SHIVANI, Mitnala SHIVANI, K.B. RENUKA and Jupally SAMHITHA

12.1. Introduction 176
12.2. Literature review 176
12.3. Methodology 178
12.4. Findings and analysis of performance 182
12.5. Conclusion 187
12.6. References 188xii Practical Graph Intelligence 1

Chapter 13. Stackelberg Game Theory with Nash Equilibrium Computation: Algorithmic Applications in Graph-based Resource Competition and Network Optimization 191
Ch. Sandeep REDDY, Guntuka Kavya KRUTHIKA, Neeraja HANNALA, Jadhav KALPANA, K. MAHITHA SRI SATWIKA and K. VIDYADHARI

13.1. Introduction 191
13.2. Literature review 192
13.3. Methodology 194
13.4. Results 198
13.5. Discussion 202
13.6. Conclusion 203
13.7. References 204

Chapter 14. Graph Intelligence-enabled Quantum–Classical Hybrid Framework for Advanced Cybersecurity Threat Detection and Analytics 207
A. AGALYA and Priyadarsini K.

14.1. Introduction 208
14.2. Related work 209
14.3. Quantum–classical threat detection framework 211
14.4. Implementation and experimental design 213
14.5. Discussion and analysis 218
14.6. Conclusion 220
14.7. References 221

Chapter 15. Graph Intelligence-driven DevOps Analytics: A Multi-Modal AI Platform for Predictive Performance Optimization 223
Sanke Stephen BABU, Yatham Chandra PRAKASH REDDY, Maguluri Durga SAI SRI, Vemareddy LOKESH and Shaik Jilani BASHA

15.1. Introduction 224
15.2. Related work 225
15.3. System architecture and design 227
15.4. Implementation and evaluation 229
15.5. Discussion and future work 233
15.6. Conclusion 234
15.7. References 235

Chapter 16. Graph Intelligence-driven Automated Software Deployment System with Integrated Testing Pipelines for Continuous Delivery 237
Sree Vardhan SAI KURRA, Addanki Lakshmi SAI ROHITH, Rapolu Chandra MAHESH BABU, Manepalli KAVYA SRI and B. Prameela RANI

16.1. Introduction 238
16.2. Literature review 240
16.3. Methodology 241
16.4. Results and discussion 243
16.5. Conclusion 245
16.6. References 246

List of Authors 249
Index 259

저자 소개

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.

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