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Graph Theory for Computer Science

Edited by Manikandan Rajagopal, Ramkumar Sivasakthivel, Joseph Varghese Kureethara, Niranjanamurthy M., and Biswadip Basu Mallik
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394302598  |  Hardcover  |  
560 pages
Price: $225 USD
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One Line Description
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.

Audience
Researchers, academia, and professionals of computer science, its related fields, and mathematics

Description
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.

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Author / Editor Details
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 optimization.

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Table of Contents
Preface
1. A Comprehensive Study on Pathfinding in Dynamic Graphs Using Automaton and Two-Way Depth-First Search
Ajayaditya L. and Anitha N.
1.1 Introduction
1.1.1 Literature Review
1.2 Preliminaries
1.2.1 Directed Acyclic Graphs (DAGs)
1.2.2 Dynamic Graphs
1.2.3 Graph Automata
1.2.4 Graph Transducers
1.3 Proposed Approach
1.3.1 Methodology
1.3.2 Dynamic DAG Representation
1.3.3 Graph Transducer for Updates
1.3.4 Two-Way Search
1.3.5 Incremental Path Updates
1.3.6 Path Optimization
1.3.7 Pruning Heuristics
1.4 Conclusion
References
2. Advancing Systemic Risk Assessment in Financial Networks with Neural Networks and Graph Labeling
Sreena T.D. and Surabhi N.V.
2.1 Introduction
2.2 Preliminaries
2.2.1 Artificial Neural Network (ANN)
2.2.2 Graph Labeling
2.2.3 Financial Performance Measures
2.3 Main Results
2.3.1 Methodology
2.3.1.1 Neural Network Model
2.3.2 Impact on Financial Risk Prediction in Collaborations
2.3.3 Python Code for Xavier Initialization
2.3.4 Gradient Calculation
2.3.4.1 Iteration I
2.3.4.2 Iteration II
2.3.4.3 Iteration III
2.3.5 Graphical Analysis
2.3.5.1 Methodology
2.3.5.2 Sigmoid Minimum Graph Labeling
2.3.5.3 Analysis
2.4 Conclusion
Bibliography
3. Advanced Image Segmentation Using Graph Cut Technique
Ramasubramanian Bhoopalan and Priyadharshini S.
3.1 Introduction
3.2 Literature Review
3.3 Challenges in Diabetic Retinopathy Image Analysis
3.3.1 Complex Morphology
3.3.2 Variability in Lesion Appearance
3.3.3 Image Quality
3.3.4 Inter-Observer Variability
3.4 Graph Cut Algorithm for Image Segmentation
3.4.1 Graph Construction
3.4.2 Energy Function Definition
3.4.3 Graph Cut Optimization
3.4.4 Segmentation Refinement
3.5 Block Diagram of Graph Cut Algorithm
3.5.1 Input Image
3.5.2 Pre-Processing
3.5.2.1 Noise Removal—Median Filter Functionality
3.5.2.2 Contrast Enhancement—Histogram Equalization
3.5.3 Feature Extraction
3.5.4 Graph Construction
3.5.5 Energy Function Definition
3.5.6 Graph Cut Optimization
3.5.6.1 Graph Representation
3.5.6.2 Source (s) and Sink (t)
3.5.6.3 Flow Function (f)
3.5.6.4 Flow Constraints
3.6 Results and Discussion
3.6.1 Qualitative Evaluation Metrics
3.6.2 Evaluation Metrics
3.6.2.1 Confusion Matrix
3.6.2.2 Sensitivity (Recall)
3.6.2.3 Specificity
3.6.2.4 Precision
3.6.2.5 F1 Score
3.6.2.6 Accuracy
3.7 Conclusion
Bibliography
4. An Encryption and Decryption of Block Ciphers Using Multipartite Graphs
A. John Kaspar, Nadar Jenita Mary Masilamani Raja, Tabitha Agnes Mangam and Saravanan V.
4.1 Introduction
4.1.1 The Importance of Cryptography
4.1.2 The Role of Graph Cryptography
4.1.3 Components of Cryptography
4.1.4 Real-World Applications
4.2 Preliminaries
4.3 Literature Review
4.4 Proposed Algorithm
4.4.1 Proposed Encryption Procedure
4.4.2 Proposed Decryption Procedure
4.5 Illustration
4.6 Conclusion
References
5. Big Data Analytics—Graph Databases and Insights
Nishant Wanjari, Aashka Gupta and Reshma Gulwani
5.1 Introduction
5.2 Role of Graph Database in Big Data
5.3 Benefits of Graph Database in Big Data
5.4 Things Needs to be Improved
5.5 Applications and Use Cases of Graph Databases
5.5.1 Geospatial System
5.5.2 Graph Database Visualization for Social Network Analysis
5.5.3 Recommendations Systems
5.5.4 Master Data Organization
5.6 Real-World Applications/Examples
5.6.1 Graph Database for Big Data in Healthcare
5.6.2 Graph Database for Big Data in Education
5.6.3 Graph Database for Big Data in IoT
5.6.4 Graph Database for Big Data in Recommendation System
5.7 Different Tools for Graph Databases
5.8 Conclusion
References
6. Implementing Various Graph Labeling Techniques to Strengthen Cryptosystem Security
Shivapriya P., K.N. Meera and Said Broumi
6.1 Introduction
6.2 Fundamental Concepts
6.3 EOHL in Enhancing Cryptosystem Security
6.3.1 A Combination of Ciphers
6.3.1.1 Encryption
6.3.1.2 Decryption
6.3.2 Using Digraph Substitution Cipher
6.3.2.1 Encryption
6.3.2.2 Decryption
6.3.3 Key Generation for CBC Mode of AES
6.3.3.1 Test for Randomness
6.3.3.2 Illustration
6.4 Conclusion
References
7. Graphs in IoT: Network Topology and Connectivity
Asha Sunilkumar
7.1 Introduction
7.2 Definitions
7.2.1 Graph
7.2.2 Directed Graphs
7.2.3 Subgraph of a Graph
7.2.4 Path and Connectedness
7.2.5 Connected and Disconnected Graphs
7.2.6 Theorem
7.2.7 Complete Graph/Clique
7.2.8 Degree of a Node
7.2.9 Distance, Radius, and Center of a Graph
7.3 Network Graphs and Its Properties
7.3.1 Network Graphs
7.3.2 Clustering Coefficient
7.3.3 Power-Law Degree Distribution
7.3.4 Centrality Metrics in a Network Graph
7.3.5 Scale-Free Networks
7.3.6 Sparse Graphs
7.3.7 Random Graphs
7.3.8 Small-World Network Graphs and Ultra-Small-World Network Graphs
7.3.9 Robustness of Network Topologies
7.3.10 Cut Nodes, Bridges, and Blocks
7.3.11 Theorem
7.3.12 Theorem
7.3.13 Intersection Graphs
7.3.14 Theorem
7.3.15 Corollary
7.3.16 Node Connectivity and Link Connectivity
7.3.17 Theorem: (Relationship Between κ ,λ,δ )
7.3.18 Theorem
7.3.19 Different Techniques to Measure Robustness of a System
7.3.20 Homogeneous Networks and Heterogeneous Networks
7.4 Attacks of Various Types on a Network
7.4.1 Random Attacks
7.4.2 Malicious Attacks
7.4.3 Resilience of a Network
7.5 Conclusion
References
8. Understanding Dependency Graphs in Parallel and Distributed Computing from Concept to Execution
S. Naganandhini, M. Vijayakumar, K. Gopalakrishnan and T. Nithya
8.1 Introduction
8.1.1 Importance in Managing Task Dependencies and Parallelism
8.2 Fundamentals of Dependency Graphs
8.2.1 Components
8.3 Types of Dependency Graphs
8.4 Applications of Dependency Graphs
8.4.1 Task Scheduling
8.4.1.1 Principles of Task Scheduling
8.4.1.2 Strategies for Task Scheduling
8.4.2 Resource Allocation and Optimization
8.4.2.1 Principles of Resource Allocation
8.4.2.2 Strategies for Resource Allocation
8.4.3 Workflow Management
8.4.3.1 Features of Workflow Management Systems
8.4.4 Dataflow Programming
8.4.4.1 Principles of Dataflow Programming
8.4.5 Real-World Examples and Use Cases
8.4.5.1 Data Processing Pipelines in Big Data Systems
8.4.5.2 Real-Time Streaming Analytics
8.4.5.3 Machine Learning Pipelines
8.4.5.4 Scientific Workflows
8.4.5.5 ETL (Extract, Transform, and Load) Processes
8.4.5.6 Image and Video Processing Pipelines
8.5 Algorithms for Dependency Graph Processing
8.5.1 Topological Sorting
8.5.2 Dependency Resolution Algorithms
8.5.3 Parallel and Distributed Graph Traversal Algorithms
8.5.4 Optimization Techniques for Large-Scale Graphs
8.5.5 Comparative Analysis
8.6 Implementing Dependency Graphs
8.6.1 Data Structures for Representing Dependency Graphs
8.6.2 Design Considerations for Efficient Implementation
8.6.3 Parallel and Distributed Implementation Strategies
8.6.4 Integration with Existing Parallel and Distributed Computing Frameworks
8.7 Case Studies and Practical Examples
8.7.1 Case Study: Task Scheduling in Distributed Systems
8.7.2 Case Study 2: Software Build Systems
8.7.3 Case Study 3: Supply Chain Management
8.8 Challenges and Future Directions in Dependency Graph Processing
8.9 Conclusion
References
9. A Comprehensive Overview on Graph‑Based Modeling of Transactions in Blockchain Technology
M. Anandaraj, V. Shanmugaveni, K. Ganesh Kumar and T. Saranya
9.1 Introduction to Blockchain Technology
9.1.1 Applications of Blockchain
9.2 Fundamentals Concepts of Graph Theory
9.2.1 Applications of Graph Theory
9.3 Transaction Modeling in Blockchain Networks
9.3.1 Transaction Flow in Blockchain Network
9.3.2 Analyzing Transaction Flows
9.3.3 Mathematical Basics and Analysis
9.4 Graph Algorithms for Blockchain Networks
9.4.1 Performance Optimization with Graph Algorithm
9.5 Graph-Based Approaches for Detecting Anomalies
9.6 Interaction Between Blockchain Layers with Graph Theoretic Perception
9.7 Enhancing Security through Graph-Based Validation
9.8 Future Directions and Research Opportunities
9.9 Conclusion
References
10. Graph Databases Unveiling Insights in Big Data Analytics
V. Balajishanmugam, S. Sumathi, J. Deepika and P. Sindhuja
10.1 Introduction
10.2 Fundamentals of Graph Theory
10.3 Graph Database Architecture
10.4 Graph Data Modeling
10.5 Graph Query Languages
10.6 Overview of Graph Analytics Algorithms
10.7 Applications Across Diverse Domains
10.8 Case Studies and Examples
10.9 Graph Visualization Techniques
10.10 Challenges and Future Directions
10.11 Conclusions
References
11. Secure Equitability in Chemical Networks
Annie Alex and V. Sangeetha
11.1 Introduction
11.2 Secure Equitable Domination in Silicate Structures
11.3 Secure Equitable Domination in Hexagonal Structures
11.4 Application
11.5 Conclusion
References
12. Fuzzy Graph Theory–Enhanced Gradient Boosting Regression with Network Flow Graphs for Effective Inventory Management Amid Shortages
K. Kalaiarasi and N. Sindhuja
12.1 Introduction
12.2 Inventory Management Challenges Amid Shortages for Businesses
12.3 Preliminaries
12.3.1 Fuzzy Set
12.3.2 Membership Functions
12.3.3 Nodes
12.3.4 Gradient Boosting Regression
12.3.5 Inventory Management with Shortages
12.3.6 Network Flow Graph
12.4 Inventory Management Analysis for Clothing Items
12.4.1 Optimizing Clothing Inventory Management with Fuzzy Graph Theory
12.4.2 Enhancing Clothing Inventory Management Via Python Visualization Utilizing Fuzzy Graph Theory
12.4.3 Optimizing Inventory Management with Gradient Boosting Regression
12.4.4 Visualizing Predicted Demand Flow in Clothing Inventory Using Network Flow Graph
12.5 Conclusion
References
13. Graph Unveiling in Image Processing: A Comprehensive Study of Recognition and Segmentation Methods in Medical Images
M. Indira, M. Midhula and S. Vishnupriya
13.1 Introduction
13.2 Literature Review
13.3 Methodology
13.3.1 Curvelet-Based Region Point Selection for Segmentation
13.3.2 K-Means Segmentation
13.4 Discussion on Results
13.4.1 Effect of Mean Square Error to Signals (MSES)
13.4.2 Impact of Segmentation Accuracy
13.5 Conclusion
Bibliography
14. From Nodes to Keys: Graph-Based Cryptosystems for Secure Communication
Meera Saraswathi, Dhanyashree, K. N. Meera and Yuqing Lin
14.1 Introduction
14.2 Graph Labeling in Cryptography: A Survey of Key Articles
14.3 Key Generation for Cryptosystems
14.3.1 Radio Mean Labeling for Cryptographic Key Generation
14.3.2 Radio Path Coloring for Cryptographic Key Generation
14.4 Concluding Remarks
References
15. Graph-Based Representation in Artificial Neural Networks
K. Swarupa Rani, Boreda Divya, Ravi Uyyala, Ravindra Changala, G. Ganesh Kumar and R. Banupriya
15.1 Introduction
15.1.1 Evolution of Artificial Neural Networks
15.1.2 Motivation for Graph-Based Representations
15.2 Fundamentals of Graphs
15.2.1 Graph Theory Basics
15.2.2 Graph Representation Learning
15.3 Graph Neural Networks (GNNs)
15.3.1 Message Passing Mechanisms
15.3.2 Node Embeddings
15.3.3 Graph Pooling
15.3.4 Graph Attention Mechanisms
15.4 Training Graph Neural Networks
15.4.1 Supervised Learning
15.4.2 Semi-Supervised Learning
15.4.3 Unsupervised Learning
15.4.4 Reinforcement Learning
15.5 Applications of Graph-Based Neural Networks
15.5.1 Social Network Analysis
15.5.2 Molecular Structure Prediction
15.5.3 Recommendation Systems
15.5.4 Knowledge Graphs
15.5.5 Computer Vision
15.5.6 Natural Language Processing
15.6 Challenges and Future Directions
15.7 Conclusion
Bibliography
16. Unleashing the Power of Graph Theory in Data Structures
P. Jayalakshmi and K. Manimekalai
16.1 Introduction
16.2 History of Graph Theory
16.3 When to Use Graph
16.4 Graph Theory Applications in Computer Science
16.4.1 Shortest Path in Road Network
16.4.2 Computer Network Security
16.4.3 Map Coloring and GSM Mobile Phone Networks
16.4.4 Bi-Processing Task
16.5 Graph Data Structure
16.5.1 Graphs and Its Types
16.6 Graphs in Data Structures
16.7 Graph Representation in Data Structure
16.8 Graph Operations in Data Structures
16.9 Graph Traversal in Data Structure
16.9.1 Breadth-First Search (BFS)
16.9.2 Depth-First Search (DFS)
16.10 Efficiency of Algorithm
16.11 Real-Time Applications of Graphs in Data Structure
16.12 Advantages of Graphs in Data Structures
16.13 Drawbacks of Graphs in Data Structures
16.14 Conclusion
References
17. Digital Payment Satisfaction Analysis Using Graph-Based Factor Analysis Technique
R. Velmurugan and Reeba, O.B.
17.1 Introduction
17.2 Review of Literature
17.3 Statement of the Problem
17.4 Objectives of the Study
17.5 Scope of the Study
17.6 Research Methodology
17.6.1 Data
17.6.2 Sampling and Sample Size
17.6.3 Tools Used
17.7 Findings
17.7.1 Socio-Economic Profile
17.7.2 Satisfaction on Using Digital Payments
17.8 Suggestions
17.9 Conclusion
17.10 Scope for Further Research
Bibliography
18. A Statistical Graph–Based Welfare Measure Estimation Provided in the Public Sector Organization
Roney Rose K. F. and Mathan Kumar. V.
18.1 Introduction
18.2 Financial Instruments
18.3 Need of the Study
18.4 Review of Literature
18.5 Research Gap
18.6 Objectives
18.7 Hypothesis
18.8 Research Methodology
18.8.1 Statutory Welfare Measures
18.8.2 Non-Statutory Welfare Measures
18.9 Results and Discussion
18.10 Findings
18.11 Conclusion
18.12 Suggestions
18.13 Scope for Further Research
Bibliography
19. A Graph Analysis Model for Predicting Stock Market Trends Using Deep Learning
J. Sudarvel, M. Kalimuthu, Atul Bansal, D. Vishnu Vardhan, T. Rajendran and S. Sridhar
19.1 Introduction
19.2 Literature Survey
19.3 Methodology
19.3.1 Data Preprocessing
19.3.2 Extracting Technical Features
19.3.3 Restricted Boltzmann Machine
19.3.4 Multi-Layer Perceptron
19.4 Results and Analysis
19.4.1 Performance Evaluation
19.5 Conclusion
References
20. A Performance Graph-Based Design, Implementation, and Evaluation of Metaverse Technology for Health Education
T. Nithya, P. Shanmugaprabha, T. Anitha, A. Priya and S. Reshmi
20.1 Introduction
20.1.1 Practical Applications of Metaverse
20.1.2 Why Metaverse in Health Education?
20.2 Existing Metaverse Applications in Health Education
20.2.1 Advantages and Limitations of Metaverse Technologies in Health Education
20.3 Review of Existing Studies and Works
20.4 Keyways to Improve Medical Education Using Metaverse
20.5 Limitations and Challenges for Metaverse in Health Education
20.6 Conclusion
References
21. An Effective Stock Market Price Graph Prediction Model Using Random Forest Algorithm
Santhosh Nithyananda, R. Sankar Ganesh , Samyuktha P.S., V. Ramadevi, J. Sudarvel and Karthick S.R.
21.1 Introduction
21.1.1 Technical Analysis
21.2 Literature Review
21.3 Methodology
21.3.1 Data Preprocessing
21.3.2 Feature Extraction
21.3.3 WOA
21.3.4 Random Forest
21.4 Results and Discussion
21.4.1 Performance Evaluation
21.5 Conclusion
References
22. Forecasting Short-Term Stock Market with Graph Prediction Model and Genetic Algorithm–Based Backpropagation Neural Network
Santhosh Nithyananda, R. Sankar Ganesh, Samyuktha, P.S., P. Easwaran and Smruthymol J.
22.1 Introduction
22.1.1 Financial Instruments
22.2 Literature Review
22.3 Methodology
22.3.1 Data Preprocessing
22.3.2 Technical Indicators
22.3.3 Genetic Algorithm
22.3.4 Backpropagation Neural Network
22.4 Experiment Analysis
22.4.1 Dataset Description
22.4.2 Perfo rmance Metrics
22.5 Conclusion
References
23. Prediction of Stock Market Prices Using Real-Time Stock Data with Graph Models and Deep Learning
NadhaSha, B. Ganesh, Ajesh Kumar, P.S., D. Vishnu Vardhan and Smruthymol J.
23.1 Introduction
23.1.1 History of Stock Market
23.1.2 Deep Learning for Stock Market
23.2 Related Works
23.3 Proposed Model
23.3.1 Data Preprocessing
23.3.2 Principal Component Analysis
23.3.3 Convolutional Neural Network
23.3.3.1 Convolutional Layer
23.3.3.2 Pooling Layer
23.3.3.3 FC Layer
23.3.3.4 Dropout
23.4 Experimental Analysis
23.4.1 Dataset Description
23.4.2 Performance Metrics
23.5 Conclusion
References
24. Graph-Based Model for Indian Stock Market Trends
Atul Bansal, K. Jothi, S. Jegadeeswari, M. Kalimuthu, T. Rajendran and S. Sridhar
24.1 Introduction
24.2 Literature Survey
24.3 Methodology
24.3.1 Data Preprocessing
24.3.2 Feature Extraction
24.3.3 SDAE
24.3.4 Logistic Regression
24.4 Results and Discussion
24.4.1 Performance Evaluation
24.5 Conclusion
References
25. Employee Satisfaction Based on Welfare Measures Using Statistical Graphs
Roney Rose K. F. and Mathan Kumar V.
25.1 Introduction
25.2 Need for Study
25.3 Scope of the Study
25.4 Statement of Problem
25.5 Review of Literature
25.6 Objectives of the Study
25.7 Research Gap
25.8 Significance of the Study
25.9 Research Methodology
25.9.1 Research Design
25.9.2 Data
25.9.3 Sample Size
25.9.4 Sampling
25.9.5 Frame Work of Analysis
25.10 Findings
25.10.1 Area of Residence
25.10.2 Age
25.10.3 Gender
25.10.4 Education Qualification
25.10.5 Monthly Income
25.10.6 Family Income
25.10.7 Monthly Expenditure
25.11 Findings
25.12 Scope for Future Study
25.13 Suggestions
25.14 Conclusion
Bibliography
26. A Graph-Based Analysis of Digital Payments and Digital Technologies
R. Velmurugan and Reeba, O.B.
26.1 Introduction
26.2 Review of Literature
26.3 Statement of the Problem
26.4 Objectives of the Study
26.5 Research Methodology
26.5.1 Data
26.5.2 Sampling and Sample Size
26.5.3 Tools Used
26.6 Findings
26.6.1 Socio-Economic Profile
26.6.2 Problems on Using Digital Payments
26.7 Suggestions
26.8 Conclusion
References
27. Network Analysis of Indian Stock Market at the Onset of Ukraine–Russia War
Anindita Bhattacharjee and Jaya Mamta Prosad
27.1 Introduction
27.1.1 Background of Network
27.1.2 Financial Network
27.2 Review of Literature
27.3 Research Methodology
27.3.1 Data
27.3.2 Methodology of Network Analysis
27.4 Result and Discussion
27.5 Conclusion
References
28. Leveraging Graph Theory for Transformative Applications in Computing and Technology
Niranjanamurthy M., Mayuri K. P., Amitha S. K. and Ranjan Kumar Mishra
28.1 Introduction
28.2 Related Work
28.3 Advanced Computer Science Using Graph Theory
28.3.1 Network System
28.3.2 Data Structure
28.3.3 Communication Network
28.4 Key Graph Theory Concepts in Communication Networks
28.5 Graph Coloring
28.6 Knowledges for Graph Theory
28.7 Image Enhancement and Filtering
28.7.1 Feature Extraction and Pattern Recognition
28.8 Programming Languages and Web Application
28.8.1 Python
28.8.2 C++
28.8.3 Java
28.8.4 R
28.8.5 Haskell and Functional Languages
28.9 Creating Websites
28.9.1 Interactive Graph Visualizations
28.9.2 Interactive Backend Graph Databases
28.9.3 Interactive Algorithmic Applications
28.9.4 Interactive Educational Platforms
28.9.5 Interactive Full-Stack Integration
28.10 Conclusion
References
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