A New Approach Based on Centrality Value in Solving the Minimum Vertex Cover Problem: Malatya Centrality Algorithm

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS Computer Science-AGH Pub Date : 2022-11-27 DOI:10.53070/bbd.1195501
A. Karcı, Selman Yakut, Furkan Öztemiz
{"title":"A New Approach Based on Centrality Value in Solving the Minimum Vertex Cover Problem: Malatya Centrality Algorithm","authors":"A. Karcı, Selman Yakut, Furkan Öztemiz","doi":"10.53070/bbd.1195501","DOIUrl":null,"url":null,"abstract":"The graph is a data structures and models that used to describe many real-world problems. Many engineering problems, such as safety and transportation, have a graph-like structure and are based on a similar model. Therefore, these problems can be solved using similar methods to the graph data model. Vertex cover problem that is used in modeling many problems is one of the important NP-complete problems in graph theory. Vertex-cover realization by using minimum number of vertex is called Minimum Vertex Cover Problem (MVCP). Since MVCP is an optimization problem, many algorithms and approaches have been proposed to solve this problem. In this article, Malatya algorithm, which offers an effective solution for the vertex-cover problem, is proposed. Malatya algorithm offers a polynomial approach to the vertex cover problem. In the proposed approach, MVCP consists of two steps, calculating the Malatya centrality value and selecting the covering nodes. In the first step, Malatya centrality values are calculated for the nodes in the graph. These values are calculated using Malatya algorithm. Malatya centrality value of each node in the graph consists of the sum of the ratios of the degree of the node to the degrees of the adjacent nodes. The second step is a node selection problem for the vertex cover. The node with the maximum Malatya centrality value is selected from the nodes in the graph and added to the solution set. Then this node and its coincident edges are removed from the graph. Malatya centrality values are calculated again for the new graph, and the node with the maximum Malatya centrality value is selected from these values, and the coincident edges to this node are removed from the graph. This process is continued until all the edges in the graph are covered. It is shown on the sample graph that the proposed Malatya algorithm provides an effective solution for MVCP. Successful test results and analyzes show the effectiveness of Malatya algorithm.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science-AGH","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53070/bbd.1195501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 1

Abstract

The graph is a data structures and models that used to describe many real-world problems. Many engineering problems, such as safety and transportation, have a graph-like structure and are based on a similar model. Therefore, these problems can be solved using similar methods to the graph data model. Vertex cover problem that is used in modeling many problems is one of the important NP-complete problems in graph theory. Vertex-cover realization by using minimum number of vertex is called Minimum Vertex Cover Problem (MVCP). Since MVCP is an optimization problem, many algorithms and approaches have been proposed to solve this problem. In this article, Malatya algorithm, which offers an effective solution for the vertex-cover problem, is proposed. Malatya algorithm offers a polynomial approach to the vertex cover problem. In the proposed approach, MVCP consists of two steps, calculating the Malatya centrality value and selecting the covering nodes. In the first step, Malatya centrality values are calculated for the nodes in the graph. These values are calculated using Malatya algorithm. Malatya centrality value of each node in the graph consists of the sum of the ratios of the degree of the node to the degrees of the adjacent nodes. The second step is a node selection problem for the vertex cover. The node with the maximum Malatya centrality value is selected from the nodes in the graph and added to the solution set. Then this node and its coincident edges are removed from the graph. Malatya centrality values are calculated again for the new graph, and the node with the maximum Malatya centrality value is selected from these values, and the coincident edges to this node are removed from the graph. This process is continued until all the edges in the graph are covered. It is shown on the sample graph that the proposed Malatya algorithm provides an effective solution for MVCP. Successful test results and analyzes show the effectiveness of Malatya algorithm.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种基于中心值求解最小顶点覆盖问题的新方法:Malatya中心算法
图形是一种数据结构和模型,用于描述许多现实世界中的问题。许多工程问题,如安全和运输,都有类似的图形结构,并基于类似的模型。因此,可以使用与图数据模型类似的方法来解决这些问题。顶点覆盖问题是图论中一个重要的NP完全问题,用于建模许多问题。利用最小顶点数实现顶点覆盖被称为最小顶点覆盖问题(MVCP)。由于MVCP是一个优化问题,已经提出了许多算法和方法来解决这个问题。本文提出了Malatya算法,它为顶点覆盖问题提供了一种有效的解决方案。Malatya算法为顶点覆盖问题提供了一种多项式方法。在所提出的方法中,MVCP由两个步骤组成,即计算Malatya中心值和选择覆盖节点。在第一步中,计算图中节点的Malatya中心值。这些值是使用Malatya算法计算的。图中每个节点的Malatya中心值由该节点的度与相邻节点的度之和组成。第二步是顶点覆盖的节点选择问题。从图中的节点中选择Malatya中心值最大的节点,并将其添加到解集中。然后,该节点及其重合边将从图形中删除。对于新的图,再次计算Malatya中心性值,并且从这些值中选择具有最大Malatya中央性值的节点,并且从图中去除与该节点重合的边。此过程将继续进行,直到覆盖图形中的所有边为止。在样本图上表明,所提出的Malatya算法为MVCP提供了一个有效的解决方案。成功的测试结果和分析表明了Malatya算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Computer Science-AGH
Computer Science-AGH COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.40
自引率
0.00%
发文量
18
审稿时长
20 weeks
期刊最新文献
A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm Database Replication for Disconnected Operations with Quasi Real-Time Synchronization Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints A Survey on Multi-Objective Based Parameter Optimization for Deep Learning Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1