Graph Theoretic Concepts in Data Science, 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 408
Special Issue Editor
Interests: algorithmic and structural graph theory; graph-based data science; diffusion models in graphs; network analysis and mining; network optimisation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the mining of unstructured data became a relevant field in both science and practical applications. Hidden graph structures have proven to be very useful in the analysis of seemingly unstructured data sets. The graph representation of these data makes it possible to use powerful methods of graph data science, and gives inspiration to develop new methods.
Network science and graph mining have become key disciplines providing the methodology for processing and analysing graph structured data. The powerful methods of the above fields made it possible to discover the patterns and describe the dynamics of networked systems. On the other hand, although the classical field of graph theory ensures the modelling framework, the recent progress in discrete models and methods such as algorithmic and structural graph theory still has limited use in data science.
The aim of this Special Issue is to provide a forum for researchers in graph theory and data science to share their original results about the integrated use of both fields. Graph theoretic papers motivated by data science problems as well as data science applications based on graph theoretic solutions are both welcome.
Dr. Miklós Krész
Guest Editor
Manuscript Submission Information
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Keywords
- data science
- graph models
- graph algorithms
- network analytics
- big data problems
- large graphs
- computational complexity
