双曲平面上无标度网络的力定向嵌入

Thomas Bläsius, T. Friedrich, Maximilian Katzmann
{"title":"双曲平面上无标度网络的力定向嵌入","authors":"Thomas Bläsius, T. Friedrich, Maximilian Katzmann","doi":"10.4230/LIPIcs.SEA.2021.22","DOIUrl":null,"url":null,"abstract":"Force-directed drawing algorithms are the most commonly used approach to visualize networks. While they are usually very robust, the performance of Euclidean spring embedders decreases if the graph exhibits the high level of heterogeneity that typically occurs in scale-free real-world networks. As heterogeneity naturally emerges from hyperbolic geometry (in fact, scale-free networks are often perceived to have an underlying hyperbolic geometry), it is natural to embed them into the hyperbolic plane instead. Previous techniques that produce hyperbolic embeddings usually make assumptions about the given network, which (if not met) impairs the quality of the embedding. It is still an open problem to adapt force-directed embedding algorithms to make use of the heterogeneity of the hyperbolic plane, while also preserving their robustness. We identify fundamental differences between the behavior of spring embedders in Euclidean and hyperbolic space, and adapt the technique to take advantage of the heterogeneity of the hyperbolic plane. 2012 ACM Subject Classification Theory of computation → Random projections and metric embeddings","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"57 1","pages":"22:1-22:18"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Force-Directed Embedding of Scale-Free Networks in the Hyperbolic Plane\",\"authors\":\"Thomas Bläsius, T. Friedrich, Maximilian Katzmann\",\"doi\":\"10.4230/LIPIcs.SEA.2021.22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Force-directed drawing algorithms are the most commonly used approach to visualize networks. While they are usually very robust, the performance of Euclidean spring embedders decreases if the graph exhibits the high level of heterogeneity that typically occurs in scale-free real-world networks. As heterogeneity naturally emerges from hyperbolic geometry (in fact, scale-free networks are often perceived to have an underlying hyperbolic geometry), it is natural to embed them into the hyperbolic plane instead. Previous techniques that produce hyperbolic embeddings usually make assumptions about the given network, which (if not met) impairs the quality of the embedding. It is still an open problem to adapt force-directed embedding algorithms to make use of the heterogeneity of the hyperbolic plane, while also preserving their robustness. We identify fundamental differences between the behavior of spring embedders in Euclidean and hyperbolic space, and adapt the technique to take advantage of the heterogeneity of the hyperbolic plane. 2012 ACM Subject Classification Theory of computation → Random projections and metric embeddings\",\"PeriodicalId\":9448,\"journal\":{\"name\":\"Bulletin of the Society of Sea Water Science, Japan\",\"volume\":\"57 1\",\"pages\":\"22:1-22:18\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of the Society of Sea Water Science, Japan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4230/LIPIcs.SEA.2021.22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the Society of Sea Water Science, Japan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4230/LIPIcs.SEA.2021.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

力导向绘图算法是可视化网络最常用的方法。虽然欧几里得弹簧嵌入器通常非常健壮,但如果图形显示出在无标度的现实世界网络中通常出现的高度异质性,则欧几里得弹簧嵌入器的性能会下降。由于异质性自然地从双曲几何中出现(事实上,无标度网络通常被认为具有潜在的双曲几何),因此将它们嵌入双曲平面是很自然的。以前产生双曲嵌入的技术通常对给定的网络进行假设,如果不满足这些假设,就会损害嵌入的质量。如何使力定向嵌入算法在利用双曲平面的异构性的同时保持其鲁棒性,仍然是一个有待解决的问题。我们确定了弹簧嵌入器在欧几里得空间和双曲空间中行为的根本区别,并采用该技术来利用双曲平面的非均匀性。2012 ACM学科分类计算理论→随机投影和度量嵌入
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Force-Directed Embedding of Scale-Free Networks in the Hyperbolic Plane
Force-directed drawing algorithms are the most commonly used approach to visualize networks. While they are usually very robust, the performance of Euclidean spring embedders decreases if the graph exhibits the high level of heterogeneity that typically occurs in scale-free real-world networks. As heterogeneity naturally emerges from hyperbolic geometry (in fact, scale-free networks are often perceived to have an underlying hyperbolic geometry), it is natural to embed them into the hyperbolic plane instead. Previous techniques that produce hyperbolic embeddings usually make assumptions about the given network, which (if not met) impairs the quality of the embedding. It is still an open problem to adapt force-directed embedding algorithms to make use of the heterogeneity of the hyperbolic plane, while also preserving their robustness. We identify fundamental differences between the behavior of spring embedders in Euclidean and hyperbolic space, and adapt the technique to take advantage of the heterogeneity of the hyperbolic plane. 2012 ACM Subject Classification Theory of computation → Random projections and metric embeddings
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信