Edited by
Pramod Singh Rathore, Manipal University Jaipur, India.
Abhishek Kumar, Chandigarh University, India.
Priya Batta, Amity University Punjab - Mohali, India.
Inam Ul Haq, CGC University Mohali - Punjab, India.
ISBN : 9781836691402
Publication Date : October 2026
Hardcover 294 pp
170 USD
Co-publisher
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.
1. Graph-Theoretic Foundations for Semantic Network Construction Through Transformer-based Feature Learning and Multi-Lingual Entity-Relation Graph Modeling, Vanampalli Mounika, Kotla Lakshmi Sravanthi, Kumkuma Praneetha, Nadipi Samskruthi, Kodamanchili Varshitha and Karukula Manisha.
2. Sparse Tucker Decomposition with L1 Regularization: Matrix Completion in Tensor Networks, T. Srikanth, E. Chandana, A. Manjusha, B. Pranitha, D. Varshitha and Lokam Harika.
3. Principal Component Analysis (PCA) and t-SNE Combined with Autoencoder Embeddings for Graph-based Feature Engineering and Dimensionality Reduction, P.V.S. Swojanya, Lokam Harika, Jangannagari Divya, Manyada Akanksha, Mandhadi Ashwini and Karri Lahari.
4. Instrumental Variable Regression and Double Machine Learning for Causal Effect Estimation in Graph-based Business Analytics, P.V.S. Swojanya, Kuthuru Varalaxmi, Gurka Rupa Sri, Kodiripaka Akshaya, J. Salony Pawar and Mallisetti Sai Nikhitha.
5. Gradient Boosting Machines (XGBoost, LightGBM) with Stacked Generalization for Multi-Task Learning in Graph-based Predictive Analytics, L. Srinivasa Reddy, Madhagani Siri Dhathrika, Guguloth Sindhukeerthana, Kadari Rekha, K. Bhargavi and G. Soni.
6. Spectral Graph Convolutional Networks for IoT Device Clustering and Anomalous Node Detection in Complex Network Topologies, S. Priyadharsini, R. Venkatesh, T. Sivaprakasham, Iyappan Murugesan, K. Sivaprasath and Jegan Chellakannu.
7. Graph Neural Networks with Attention Mechanisms for Customer Segmentation and Churn Prediction in E-Commerce Platforms, Korra Srinivas, Barla Meghana, Sumana Sri Aashily, Bandla Siri, A. Srividya and E. Sahithi.
8. Domain Adaptation via Maximum Mean Discrepancy (MMD) and Adversarial Domain Discriminators for Graph Neural Network Transfer Learning, T. Kavitha, Kurri Sravani, Mandha Nandhini, Kothapally Shirish? Reddy, Muddam Akhila and Jilla Taruni.
9. Graph Intelligence-driven Reinforcement Learning Architecture for Modeling and Control of Microfluidic Transport Phenomena and Nonlinear Heat–Mass Coupled Nanofluid Flows, L. Manjula, K. Ramachandran, T.R.K. Kumar, S. Leoni Sharmila, R. Balapriya and R. Vanaja.
10. Graph Intelligence-based Numerical Solutions using Runge–Kutta and Caputo Fractional Derivatives for Nonlinear Biological Transport Equations, T. Srikanth, Alla Asritha, A. Vaishnavi, Batchu Manaswi, B. Sathvika and Chandragiri Sushma.
11. Mixed-Integer Linear Programming (MILP) with Column Generation for Vehicle Routing Problems with Time Windows Using Graph-based Route Optimization, M. Chandrarao, K. Jagruthi, Kandlapally Usha Sri, Ledalla Himavarsha, K.H. Shreya and K. Sahithi.
12. Differential Evolution and Grey Wolf Optimization: Hybrid Metaheuristics for Constrained Non-Convex Problems in Graph-based Network Optimization, Anil Jawalkar, Gunnam Harshini, Macharla Shivani, Mitnala Shivani, K.B. Renuka and Jupally Samhitha.
13. Stackelberg Game Theory with Nash Equilibrium Computation: Algorithmic Applications in Graph-based Resource Competition and Network Optimization, Ch. Sandeep Reddy, Guntuka Kavya Kruthika, Neeraja Hannala, Jadhav Kalpana, K. Mahitha Sri Satwika and K. Vidyadhari.
14. Graph Intelligence-enabled Quantum–Classical Hybrid Framework for Advanced Cybersecurity Threat Detection and Analytics, A. Agalya and Priyadarsini K.
15. Graph Intelligence-driven DevOps Analytics: A Multi-Modal AI Platform for Predictive Performance Optimization, Sanke Stephen Babu, Yatham Chandra Prakash Reddy, Maguluri Durga Sai Sri, Vemareddy Lokesh and Shaik Jilani Basha.
16. Graph Intelligence-driven Automated Software Deployment System with Integrated Testing Pipelines for Continuous Delivery, Sree Vardhan Sai Kurra, Addanki Lakshmi Sai Rohith, Rapolu Chandra Mahesh Babu, Manepalli Kavya Sri and B. Prameela Rani.
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.
