Guest Editors
Prof. Ali Ahmad, Jazan University, Saudi Arabia.
Prof. Andrea Semanicová-Fenovcíková, Technical University, Slovakia.
Prof. Muhammad Ahsan Asim, University of Missouri-Kansas City (UMKC), USA.
Summary
Algorithmic solutions are devised to enhance the usability of discrete structures particularly graphs and trees. Interdisciplinary research among discrete mathematics and computer science has evolved many useful applications. Algorithms help in solving many problems, where other mathematical solutions are very complex or impossible. Computations help in tackling numerous issues, where other numerical arrangements are extremely perplexing or incomprehensible. Algorithms designing is a major field in computer science, because later on these algorithms can easily be programmed and implemented in computer languages. Appropriate selection of algorithm design architecture (iterative or recursive) and algorithm design strategy ( brute-force , divide & conquer, heuristics approach or dynamic programming ) lead to design an efficient algorithm that can produce desired output in optimum time. Computational complexity is based on two parameters that are time and space complexity. Results of computational complexity help to select or reject an algorithm according to available resources.
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Also, Graph Database Management Systems enhance support for graph data models by providing data loading, data conversion, consistency, security and maintenance, along with the ability to provision clusters that scale up and scale out.
Graphs are used for modelling multiple relations and processes in computer, engineering, physical, and biological sciences. Moreover, they are also used in information systems, social sciences, and many other branches of basic and applied sciences. Researchers model their problems by using graph structure in different fields of sciences. They try to find the solution of their problem by different methods like computational technique, algorithmic approach, etc. The graph theory has many applications in different subjects. The different parameters involved in graph theory including graph representations using computer systems and graph-theoretic data structures such as list structure and matrix structure.
Nowadays research in the area of graph labeling is abundantly expanding in depth and breadth. According to depth-research a specific type of labeling is studied on more complex families of graphs or open problems. Whereas in breadth-research more and more types of labeling are identified by variation of graph invariants or by augmenting existing types of labeling.
The aim of this Special Issue is to bring together original research and review articles that discuss graph theory applications in aspects like location algorithms/positions, Data science, Machine learning, Computer programming, Database managements, graph coloring etc.
Keywords
Potential topics include but are not limited to the following:
Graph algorithms and complexity theory
Number theory and computer security
Locating numbers
Image segmentation
Resolvability and its parameters
Distance in graphs
Data structures and algorithm for labeling graph
Spectrum of a Graph
Eigenvalues and Eigenvectors
Topological indices
Algebraic construction of the extremal graph
Graph convolutional networks
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