Graph Theoretic Methods in Scientific Computing & Industrial Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 6862
Special Issue Editors
Interests: graph theory; combinatorial optimization; algorithms; high performacne computing
Interests: graph theory; data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the field of scientific computing, the concept of discrete models and methods has recently become a key approach. Graphs play a central role in both their modeling and efficient algorithmic arsenal. Intensive research has been pursued on graph algorithms, complex networks, large graphs and hard complexity class problems. The aim of the research community is to achieve a complex approach using graph-theoretical concepts that can integrate these topics. From the algorithmic perspective, there are huge differences in problem hardness. Some problems can be solved very quickly in polynomial time, such as finding the shortest path in a set of Google Maps directions. Other problems are much harder (i.e., NP-hard), such as clique search. Keen interest is currently directed towards the preconditioning and kernelization of these problems.
Currently, two main challenges are in the focus of future solutions. On one hand, there are those problems which inherently have the features of natural graph models. To mention a few examples, distances measured and clusters searched in network models for roads, social interactions or chemical interactions in complex systems as the human body. These require scalable methods for large graphs as well as the integration of intelligent technologies with graph-theoretic methods (e.g., machine learning). On the other hand, graphs can be considered a “modeling language” like numbers by representing hidden relationships that are far from obvious. In the case of these types of problems, the graph-building methodology is problem-specific. For analysis of the stock market, the market graph is built from the pairwise temporal correlation of stock price. Special graphs are constructed for proving mathematical conjectures (e.g., Keller’s conjecture). Auxiliary graphs can represent scheduling problems, protein docking sites and many more. Effective modeling can be far from trivial, and sporadic results have recently been achieved.
The objective of this Special Issue is to provide an opportunity for researchers focusing on both effective novel graph models for various applications fields as well as scalable efficient graph algorithms targeting the computation of solutions for complex graph-theoretic problems.
We welcome innovative contributions on all areas of graph theory and its applications. Topics include, but are not limited to:
- Efficient graph algorithms—serial and parallel;
- Heuristic graph algorithms for large graphs;
- Exact algorithms for hard graph problems;
- Preconditioning and kernelization of graph problems;
- Machine learning aiding graph algorithms;
- Modeling potential of graphs;
- Application of graph algorithms;
- Graph-theoretical concepts in scientific computing.
Dr. Bogdan Zavalnij
Dr. Miklós Krész
Prof. Dr. Sándor Szabó
Guest Editors
Manuscript Submission Information
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