Special Issue on “Graph Algorithms and Applications”

Serafino Cicerone,Gabriele Di Stefano

doi:10.3390/a14050150

Abstract

The mixture of data in real life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. For instance, more recently, graphs have found new applications in solving problems for emerging research fields such as social network analysis, design of robust computer network topologies, frequency allocation in wireless networks, and bioinformatics. Unfortunately, the related algorithms usually suffer from high computational complexity, since some of these problems are NP-hard. Therefore, in recent years, many graph models and optimization algorithms have been proposed to achieve a better balance between efficacy and efficiency. The aim of this Special Issue is to provide an opportunity for researchers and engineers from both academia and the industry to publish their latest and original results on graph models, algorithms, and applications to problems in the real world, with a focus on optimization and computational complexity.

Highlights

Graphs represent mathematical abstractions that can be used to represent networks of various types: physical, biological, or social. This led the development of algorithmic graph theory as a classical research area in computer science
It focuses on the discovery of characterization theorems on graphs, which in turn often lead to the development of efficient algorithms for practical problems that can be modeled on graphs
We summarize the contents of all six published papers

Summary

Introduction

Graphs represent mathematical abstractions that can be used to represent networks of various types: physical (e.g., the Internet or transportation networks), biological (e.g., brain networks), or social (e.g., online social networks). It focuses on the discovery of characterization theorems on (different types of) graphs, which in turn often lead to the development of efficient algorithms for practical problems that can be modeled on graphs. In [2], the authors used graph theory models to cope with problems arising in the field of molecular biology and bioinformatics.

Results

Conclusion