This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines.
Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic relationships in natural language processing, a technology behind many businesses. Disciplinary and industry developments are seeing a major transition towards more interconnected and data-driven decision-making, and the application of graph theory will facilitate this transition. Disciplines such as parallel and distributive computing will gain insights into how graph theory can help with resource optimization and job scheduling, creating considerable change in the design and development of scalable systems. This book provides comprehensive coverage of how graph theory acts as the thread that connects different areas of computer science to create innovative solutions to modern technological problems. Using a multi-faceted approach, the book explores the fundamentals and role of graph theory in molding complex computational processes across a wide spectrum of computer science.
Table of Contents:
Preface xxi
1 A Comprehensive Study on Pathfinding in Dynamic Graphs Using Automaton and Two-Way Depth-First Search 1
Ajayaditya L. and Anitha N.
2 Advancing Systemic Risk Assessment in Financial Networks with Neural Networks and Graph Labeling 17
Sreena T.D. and Surabhi N.V.
3 Advanced Image Segmentation Using Graph Cut Technique 35
Ramasubramanian Bhoopalan and Priyadharshini S.
4 An Encryption and Decryption of Block Ciphers Using Multipartite Graphs 53
A. John Kaspar, Nadar Jenita Mary Masilamani Raja, Tabitha Agnes Mangam and Saravanan V.
5 Big Data Analytics—Graph Databases and Insights 69
Nishant Wanjari, Aashka Gupta and Reshma Gulwani
6 Implementing Various Graph Labeling Techniques to Strengthen Cryptosystem Security 93
Shivapriya P., K.N. Meera and Said Broumi
7 Graphs in IoT: Network Topology and Connectivity 109
Asha Sunilkumar
8 Understanding Dependency Graphs in Parallel and Distributed Computing from Concept to Execution 129
S. Naganandhini, M. Vijayakumar, K. Gopalakrishnan and T. Nithya
9 A Comprehensive Overview on Graph‑Based Modeling of Transactions in Blockchain Technology 161
M. Anandaraj, V. Shanmugaveni, K. Ganesh Kumar and T. Saranya
10 Graph Databases Unveiling Insights in Big Data Analytics 187
V. Balajishanmugam, S. Sumathi, J. Deepika and P. Sindhuja
11 Secure Equitability in Chemical Networks 201
Annie Alex and V. Sangeetha
12 Fuzzy Graph Theory–Enhanced Gradient Boosting Regression with Network Flow Graphs for Effective Inventory Management Amid Shortages 213
K. Kalaiarasi and N. Sindhuja
13 Graph Unveiling in Image Processing: A Comprehensive Study of Recognition and Segmentation Methods in Medical Images 233
M. Indira, M. Midhula and S. Vishnupriya
14 From Nodes to Keys: Graph-Based Cryptosystems for Secure Communication 253
Meera Saraswathi, Dhanyashree, K. N. Meera and Yuqing Lin
15 Graph-Based Representation in Artificial Neural Networks 267
K. Swarupa Rani, Boreda Divya, Ravi Uyyala, Ravindra Changala, G. Ganesh Kumar and R. Banu Priya
16 Unleashing the Power of Graph Theory in Data Structures 287
P. Jayalakshmi and K. Manimekalai
17 Digital Payment Satisfaction Analysis Using Graph-Based Factor Analysis Technique 315
R. Velmurugan and Reeba, O.B.
18 A Statistical Graph–Based Welfare Measure Estimation Provided in the Public Sector Organization 329
Roney Rose K. F. and Mathan Kumar. V.
19 A Graph Analysis Model for Predicting Stock Market Trends Using Deep Learning 345
J. Sudarvel, M. Kalimuthu, Atul Bansal, D. Vishnu Vardhan, T. Rajendran and S. Sridhar
20 A Performance Graph-Based Design, Implementation, and Evaluation of Metaverse Technology for Health Education 361
T. Nithya, P. Shanmugaprabha, T. Anitha, A. Priya and S. Reshmi
21 An Effective Stock Market Price Graph Prediction Model Using Random Forest Algorithm 381
Santhosh Nithyananda, R. Sankar Ganesh, Samyuktha P.S., V. Ramadevi, J. Sudarvel and Karthick S.R.
22 Forecasting Short-Term Stock Market with Graph Prediction Model and Genetic Algorithm–Based Backpropagation Neural Network 395
Santhosh Nithyananda, R. Sankar Ganesh, Samyuktha, P.S., P. Easwaran and Smruthymol J.
23 Prediction of Stock Market Prices Using Real-Time Stock Data with Graph Models and Deep Learning 411
NadhaSha, B. Ganesh, Ajesh Kumar, P.S., D. Vishnu Vardhan and Smruthymol J.
24 Graph-Based Model for Indian Stock Market Trends 431
Atul Bansal, K. Jothi, S. Jegadeeswari, M. Kalimuthu, T. Rajendran and S. Sridhar
25 Employee Satisfaction Based on Welfare Measures Using Statistical Graphs 449
Roney Rose K. F. and Mathan Kumar V.
26 A Graph-Based Analysis of Digital Payments and Digital Technologies 475
R. Velmurugan and Reeba, O.B.
27 Network Analysis of Indian Stock Market at the Onset of Ukraine–Russia War 489
Anindita Bhattacharjee and Jaya Mamta Prosad
28 Leveraging Graph Theory for Transformative Applications in Computing and Technology 503
Niranjanamurthy M., Mayuri K. P., Amitha S. K. and Ranjan Kumar Mishra
References 522
About the Editors 525
Index 529
About the Author :
Manikandan Rajagopal, PhD is an Associate Professor at Christ University with more than a decade of research experience. He has published three textbooks, more than ten book chapters, and 15 journal articles in reputed journals and conferences. His areas of interest include data mining, optimization techniques, semantic mining, and intelligent agents.
Ramkumar Sivasakthivel, PhD is an Associate Professor at Christ University with more than 12 years of experience. He has published four textbooks, several papers in international journals and conferences, and has been granted two patents. His fields of interest are biosignal processing, artificial intelligence, human-computer interface, brain-computer interface, and machine vision.
Joseph Varghese Kureethara, PhD is a Professor of Mathematics at Christ University with more than 17 years of experience in research and teaching. He has published more than 230 articles in international journals and conferences, co-edited five books, and authored six books. He has also delivered invited talks at over fifty conferences and workshops and serves as a member of several institutions' boards.
Niranjanamurthy M., PhD is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at the BMS Institute of Technology and Management with more than 13 years of experience. He has published more than 95 articles in various national and international journals and conferences and filed 30 patents. His areas of interest are data science, machine learning, e-commerce, software testing, and software engineering.
Biswadip Basu Mallik, PhD is an Associate Professor of Mathematics in the Department of Basic Sciences and Humanities at the Institute of Engineering and Management with more than 22 years of experience. He has published five textbooks, thirteen edited books, five patents, and several research papers and book chapters in various scientific journals. His fields of research work include computational fluid dynamics, mathematical modelling, machine learning, and optimizatio
