Graph-Theoretical Algorithms and Hybrid/Collaborative Technologies
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 4209
Special Issue Editors
Interests: big data analytics; graph theoretical algorithms; life science applications
Special Issue Information
Dear Colleagues,
Graph theory provides a universal representational language for describing variables and their relationships within complex systems. Accordingly, graph-theoretical algorithms find application across a vast assortment of science and engineering domains. Modern methods of high throughput data capture, however, can challenge the pivotal utility of these methods. This is because immense, complex, and highly inhomogeneous data can render stand-alone techniques poorly suited to providing deep functional insight. Thus, there is a growing demand for and interest in augmenting graph-theoretical algorithms with complementary, integrative, or supporting technologies. Recent technical meetings such as GrAML and GrAPL workshops serve as fitting examples of this trend.
The primary aim of this Special Issue is to provide a forum for original research contributions that highlight this area of research, and that describe highly innovative ways in which graph-theoretical algorithms can work in tandem with collaborative methodologies to solve problems that only such hybrid combinations can address. We seek papers that advance the forefront of knowledge, focusing on natural synergies between graph-theoretical algorithms and complementary technologies, broadly interpreted. Machine learning is a timely example. Bayesian analysis, constraint satisfaction, mathematical programming, parallel computing, and statistical pre- and post- processing are but a few others. Both theoretical and applications-oriented papers are encouraged.
Prof. Dr. Michael A. Langston
Prof. Dr. Bradley J. Rhodes
Guest Editors
Manuscript Submission Information
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Keywords
- graph theory
- combinatorial optimization
- big data analytics
- hybrid analytic frameworks
- collaborative technologies
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