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Algorithms
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Advanced Graph Algorithms
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Advanced Graph Algorithms

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 (1 September 2022) | Viewed by 7879

Special Issue Editor

Dr. Mordechai Shalom

E-Mail Website
Guest Editor
Department of Computer Engineering, Işık University, Istanbul 34980, Turkey
Interests: algorithmic graph theory; approximation algorithms; online algorithms; combinatorial optimization

Special Issue Information

Dear Colleagues,

You are invited to submit your original research on Graph Algorithms to our Special Issue. Submissions with new algorithmic results as well submissions focusing on complexity results are welcome. We are looking for both theoretical algorithmic research work and non-trivial practical applications of existing algorithms. Possible subjects include but are not limited to classical graph algorithmic subjects such as coloring, matching, network flows, network construction, routing, graph enumerations, and intersection models. Submitted work may present solutions using, approximation, parameterized, and game theoretic techniques and problems, among others, with an on-line or distributed nature.

Dr. Mordechai Shalom
Guest Editor

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 submissions that pass pre-check are 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 1600 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

  • approximation algorithms
  • parameterized algorithms
  • algorithmic game theory
  • on-line algorithms
  • competitive analysis
  • intersection models

Published Papers (4 papers)

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Research

16 pages, 4722 KiB  
Article
Calculating the Moore–Penrose Generalized Inverse on Massively Parallel Systems
by Vukašin Stanojević, Lev Kazakovtsev, Predrag S. Stanimirović, Natalya Rezova and Guzel Shkaberina
Algorithms 2022, 15(10), 348; https://doi.org/10.3390/a15100348 - 27 Sep 2022
Cited by 3 | Viewed by 1961
Abstract
In this work, we consider the problem of calculating the generalized Moore–Penrose inverse, which is essential in many applications of graph theory. We propose an algorithm for the massively parallel systems based on the recursive algorithm for the generalized Moore–Penrose inverse, the generalized [...] Read more.
In this work, we consider the problem of calculating the generalized Moore–Penrose inverse, which is essential in many applications of graph theory. We propose an algorithm for the massively parallel systems based on the recursive algorithm for the generalized Moore–Penrose inverse, the generalized Cholesky factorization, and Strassen’s matrix inversion algorithm. Computational experiments with our new algorithm based on a parallel computing architecture known as the Compute Unified Device Architecture (CUDA) on a graphic processing unit (GPU) show the significant advantages of using GPU for large matrices (with millions of elements) in comparison with the CPU implementation from the OpenCV library (Intel, Santa Clara, CA, USA). Full article
(This article belongs to the Special Issue Advanced Graph Algorithms)
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15 pages, 1433 KiB  
Article
SoftNet: A Package for the Analysis of Complex Networks
by Caterina Fenu, Lothar Reichel and Giuseppe Rodriguez
Algorithms 2022, 15(9), 296; https://doi.org/10.3390/a15090296 - 23 Aug 2022
Cited by 1 | Viewed by 1382
Abstract
Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators in various papers. This [...] Read more.
Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators in various papers. This paper describes a MATLAB toolbox for computing such centrality indices using efficient numerical algorithms based on the connection between the Lanczos method and Gauss-type quadrature rules. Full article
(This article belongs to the Special Issue Advanced Graph Algorithms)
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21 pages, 2055 KiB  
Article
An Online Algorithm for Routing an Unmanned Aerial Vehicle for Road Network Exploration Operations after Disasters under Different Refueling Strategies
by Lorena Reyes-Rubiano, Jana Voegl and Patrick Hirsch
Algorithms 2022, 15(6), 217; https://doi.org/10.3390/a15060217 - 20 Jun 2022
Cited by 1 | Viewed by 1858
Abstract
This paper is dedicated to studying on-line routing decisions for exploring a disrupted road network in the context of humanitarian logistics using an unmanned aerial vehicle (UAV) with flying range limitations. The exploration aims to extract accurate information for assessing damage to infrastructure [...] Read more.
This paper is dedicated to studying on-line routing decisions for exploring a disrupted road network in the context of humanitarian logistics using an unmanned aerial vehicle (UAV) with flying range limitations. The exploration aims to extract accurate information for assessing damage to infrastructure and road accessibility of victim locations in the aftermath of a disaster. We propose an algorithm to conduct routing decisions involving the aerial and road network simultaneously, assuming that no information about the state of the road network is available in the beginning. Our solution approach uses different strategies to deal with the detected disruptions and refueling decisions during the exploration process. The strategies differ mainly regarding where and when the UAV is refueled. We analyze the interplay of the type and level of disruption of the network with the number of possible refueling stations and the refueling strategy chosen. The aim is to find the best combination of the number of refueling stations and refueling strategy for different settings of the network type and disruption level. Full article
(This article belongs to the Special Issue Advanced Graph Algorithms)
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15 pages, 1543 KiB  
Article
Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction
by Fadi Dornaika and Abdelmalik Moujahid
Algorithms 2022, 15(6), 207; https://doi.org/10.3390/a15060207 - 14 Jun 2022
Cited by 3 | Viewed by 1829
Abstract
Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research [...] Read more.
Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Graph Algorithms)
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