# Graph Theory and Its Applications in Computing

A special issue of *Computation* (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: **31 December 2024** | Viewed by 18886

## Special Issue Editors

**Interests:**discrete mathematics; combinatorics; mathematical chemistry, particularly chemical graph theory

**Interests:**graph theory; combinatorial optimization; bioinformatics

**Interests:**information security; security game; cooperative game theory; graph theory

**Interests:**graph theory and its applications

**Interests:**data science; machine learning; graph databases

## Special Issue Information

Dear Colleagues,

Since the birth of graph theory, it has been used to advance research and solve both theoretical and practical problems in many scientific, engineering, and social disciplines. Graphs are among the most efficient and visually appealing mathematical models for dealing with a wide range of real-world situations. Although applications of graph theory have played important roles in scientific research and discoveries for a long time, the developments of applications of graph theory in the last decades have been much more significant. It is observed that graph theory has been substantially employed in the areas of computing. The primary goal of this Special Issue is to gather the articles utilizing graph-theoretical concepts, methodologies, and advantages while solving problems to demonstrate the state-of-the-art applications of graph theory. Both the original research articles and review articles within the scope of the Special Issue are welcomed.

Research involving (but not limited to) applications of graph theory in the following fields will be considered in this Special Issue.

- data structures
- algorithms
- software engineering
- databases
- data mining
- information retrieval
- image processing
- computer networks
- web graphs
- edge computing
- network sciences
- social networks
- machine learning, graph machine learning, and deep learning
- recommendation systems
- natural language processing
- knowledge graphs
- cryptography
- cyber security
- security game theory
- blockchain
- bioinformatics
- chemistry and physics
- operations research and engineering

Dr. Akbar Ali

Prof. Dr. Guojun Li

Prof. Dr. Mingchu Li

Prof. Dr. Rao Li

Dr. Colton Magnant

Prof. Dr. Madhumangal Pal*Guest Editors*

**Manuscript Submission Information**

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. *Computation* is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

## Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

**Title: **Graph-Theoretic Analysis of Biological Networks

**Authors: **Kayhan Erciyeş

**Affiliation: **Yaşar University
**Abstract: **Biological networks such as protein interaction networks, gene regulation networks and metabolic pathways are examples of complex networks which are large graphs with small-world and scale-free properties. Analysis of these networks has a profound effect on our understanding the origins of life, health and disease states of organisms, and diagnose diseases to aid the search for remedial processes. In this review, we describe main analysis methods of biological networks using graph theory by first defining main parameters such as clustering coefficient, modularity and centrality. We then survey fundamental graph clustering methods and algorithms followed by the network motif search algorithms with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function carried out by that small network and discovering such a function provides an insight to the overall function of the organism. Lastly, we review network alignment algorithms that achieve to find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor and finding such relationship may help researchers derive ancestral relationships and predict the future evolution of organisms to enable designing new drugs. We conclude by the current challenging areas of biological network analysis and using algebraic graph theory and parallel processing for high performance analysis.