基于桁架的群体搜索的偏置边缘增强法

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2024-03-15 DOI:10.1007/s11704-024-2604-8

Yuqi Li, Tao Meng, Zhixiong He, Haiyan Liu, Keqin Li

{"title":"基于桁架的群体搜索的偏置边缘增强法","authors":"Yuqi Li, Tao Meng, Zhixiong He, Haiyan Liu, Keqin Li","doi":"10.1007/s11704-024-2604-8","DOIUrl":null,"url":null,"abstract":"<p>Most truss-based community search methods are usually confronted with the fragmentation issue. We propose a Biased edge Enhancement method for Truss-based Community Search (BETCS) to address the issue. This paper mainly solves the fragmentation problem in truss community query through data enhancement. In future work, we will consider applying the methods in the text to directed graphs or dynamic graphs.</p>","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"16 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A biased edge enhancement method for truss-based community search\",\"authors\":\"Yuqi Li, Tao Meng, Zhixiong He, Haiyan Liu, Keqin Li\",\"doi\":\"10.1007/s11704-024-2604-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Most truss-based community search methods are usually confronted with the fragmentation issue. We propose a Biased edge Enhancement method for Truss-based Community Search (BETCS) to address the issue. This paper mainly solves the fragmentation problem in truss community query through data enhancement. In future work, we will consider applying the methods in the text to directed graphs or dynamic graphs.</p>\",\"PeriodicalId\":12640,\"journal\":{\"name\":\"Frontiers of Computer Science\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Computer Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11704-024-2604-8\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11704-024-2604-8","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

大多数基于桁架的群落搜索方法通常都会面临碎片化问题。针对这一问题，我们提出了一种基于桁架的社区搜索偏边增强方法（BETCS）。本文主要通过数据增强来解决桁架式社区查询中的碎片化问题。在今后的工作中，我们将考虑把文中的方法应用到有向图或动态图中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A biased edge enhancement method for truss-based community search

Most truss-based community search methods are usually confronted with the fragmentation issue. We propose a Biased edge Enhancement method for Truss-based Community Search (BETCS) to address the issue. This paper mainly solves the fragmentation problem in truss community query through data enhancement. In future work, we will consider applying the methods in the text to directed graphs or dynamic graphs.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.