Special Issue "Graph-Theoretical Algorithms and Hybrid/Collaborative Technologies"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editors

Prof. Dr. Michael A. Langston
Website
Guest Editor
Department of Electrical Engineering and Computer Science, Tickle College of Engineering, University of Tennessee, Knoxville, TN 37996-2250, USA
Interests: big data analytics; graph theoretical algorithms; life science applications
Prof. Dr. Bradley J. Rhodes
Website1 Website2
Guest Editor
BAE Systems Inc., Arlington, USA
Interests: artificial intelligence/machine learning; hybrid analytics; situational awareness and understanding; computational neuroscience; human motor control

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

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 papers will be 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. Algorithms 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 1400 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.

Keywords

  • graph theory
  • combinatorial optimization
  • big data analytics
  • hybrid analytic frameworks
  • collaborative technologies

Published Papers (1 paper)

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Research

Open AccessArticle
Complex Neutrosophic Hypergraphs: New Social Network Models
Algorithms 2019, 12(11), 234; https://doi.org/10.3390/a12110234 - 06 Nov 2019
Cited by 8
Abstract
A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us [...] Read more.
A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models. Full article
(This article belongs to the Special Issue Graph-Theoretical Algorithms and Hybrid/Collaborative Technologies)
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