解构最密集子图

Lijun Chang, Miao Qiao
{"title":"解构最密集子图","authors":"Lijun Chang, Miao Qiao","doi":"10.1145/3366423.3380033","DOIUrl":null,"url":null,"abstract":"In this paper, we aim to understand the distribution of the densest subgraphs of a given graph under the density notion of average-degree. We show that the structures, the relationships and the distributions of all the densest subgraphs of a graph G can be encoded in O(L) space in an index called the ds-Index. Here L denotes the maximum output size of a densest subgraph of G. More importantly, ds-Indexcan report all the minimal densest subgraphs of G collectively in O(L) time and can enumerate all the densest subgraphs of G with an O(L) delay. Besides, the construction of ds-Indexcosts no more than finding a single densest subgraph using the state-of-the-art approach. Our empirical study shows that for web-scale graphs with one billion edges, the ds-Indexcan be constructed in several minutes on an ordinary commercial machine.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"25 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Deconstruct Densest Subgraphs\",\"authors\":\"Lijun Chang, Miao Qiao\",\"doi\":\"10.1145/3366423.3380033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we aim to understand the distribution of the densest subgraphs of a given graph under the density notion of average-degree. We show that the structures, the relationships and the distributions of all the densest subgraphs of a graph G can be encoded in O(L) space in an index called the ds-Index. Here L denotes the maximum output size of a densest subgraph of G. More importantly, ds-Indexcan report all the minimal densest subgraphs of G collectively in O(L) time and can enumerate all the densest subgraphs of G with an O(L) delay. Besides, the construction of ds-Indexcosts no more than finding a single densest subgraph using the state-of-the-art approach. Our empirical study shows that for web-scale graphs with one billion edges, the ds-Indexcan be constructed in several minutes on an ordinary commercial machine.\",\"PeriodicalId\":20754,\"journal\":{\"name\":\"Proceedings of The Web Conference 2020\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The Web Conference 2020\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3366423.3380033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The Web Conference 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366423.3380033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

在本文中,我们的目的是在平均度的密度概念下理解给定图的最密集子图的分布。我们证明了图G的所有密集子图的结构、关系和分布都可以在O(L)空间中编码成一个叫做ds-Index的索引。其中L表示G的最密集子图的最大输出大小。更重要的是,ds- index可以在O(L)时间内集合报告G的所有最小最密集子图,并且可以以O(L)的延迟枚举G的所有最密集子图。此外,构建ds- index的成本不超过使用最先进的方法找到一个最密集的子图。我们的实证研究表明,对于具有10亿条边的网络规模图,ds- index可以在普通商用机器上几分钟内构建完成。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deconstruct Densest Subgraphs
In this paper, we aim to understand the distribution of the densest subgraphs of a given graph under the density notion of average-degree. We show that the structures, the relationships and the distributions of all the densest subgraphs of a graph G can be encoded in O(L) space in an index called the ds-Index. Here L denotes the maximum output size of a densest subgraph of G. More importantly, ds-Indexcan report all the minimal densest subgraphs of G collectively in O(L) time and can enumerate all the densest subgraphs of G with an O(L) delay. Besides, the construction of ds-Indexcosts no more than finding a single densest subgraph using the state-of-the-art approach. Our empirical study shows that for web-scale graphs with one billion edges, the ds-Indexcan be constructed in several minutes on an ordinary commercial machine.
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
自引率
0.00%
发文量
0
期刊最新文献
Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability Those who are left behind: A chronicle of internet access in Cuba Towards Automated Technologies in the Referencing Quality of Wikidata Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25 - 29, 2022 WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021
×
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