网络中中心性度量鲁棒性的一种新方法

Jens Dörpinghaus , Vera Weil , Robert Rockenfeller , Meetkumar Pravinbhai Mangroliya
{"title":"网络中中心性度量鲁棒性的一种新方法","authors":"Jens Dörpinghaus ,&nbsp;Vera Weil ,&nbsp;Robert Rockenfeller ,&nbsp;Meetkumar Pravinbhai Mangroliya","doi":"10.1016/j.ssaho.2024.101183","DOIUrl":null,"url":null,"abstract":"<div><div>Centrality measures were first introduced in the Social Network Analysis (SNA), which is widely used in the humanities and social sciences. However, recently they are receiving attention in network science in general, e.g., for knowledge graphs and artificial intelligence. Assuming that the network is not error-free or contains multiple layers, we can identify challenges for their quality. For example, historical and narrative texts in ancient languages are usually challenging for natural language processing methods and artificial intelligence technologies developed for modern languages due to their complexity and missing models. In addition, due to quiet sources, nodes and edges in the network may be missing, which might influence the results of SNA. Other aspects may also have an effect on data, as many networks include additional layers, e.g.<!--> <!-->spatial information or archaeological artifacts. This extends to knowledge graphs. In this paper, we will summarize, compare and evaluate existing and novel methods to analyze the robustness of networks. We introduce a method with different removal strategies to analyze how additional or missing layers, nodes, or edges in a random network influence centrality measures. We can show that the robustness of social networks regardless of the error measures heavily relies on network structures, which brings up several new challenges for future research. In general, we may assume networks to be rather robust against small errors and few missing data if they follow a scale-free distribution. The results of this paper are not limited to social networks but can be applied in all fields working with centrality measures.</div></div>","PeriodicalId":74826,"journal":{"name":"Social sciences & humanities open","volume":"11 ","pages":"Article 101183"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel approach towards the robustness of centrality measures in networks\",\"authors\":\"Jens Dörpinghaus ,&nbsp;Vera Weil ,&nbsp;Robert Rockenfeller ,&nbsp;Meetkumar Pravinbhai Mangroliya\",\"doi\":\"10.1016/j.ssaho.2024.101183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Centrality measures were first introduced in the Social Network Analysis (SNA), which is widely used in the humanities and social sciences. However, recently they are receiving attention in network science in general, e.g., for knowledge graphs and artificial intelligence. Assuming that the network is not error-free or contains multiple layers, we can identify challenges for their quality. For example, historical and narrative texts in ancient languages are usually challenging for natural language processing methods and artificial intelligence technologies developed for modern languages due to their complexity and missing models. In addition, due to quiet sources, nodes and edges in the network may be missing, which might influence the results of SNA. Other aspects may also have an effect on data, as many networks include additional layers, e.g.<!--> <!-->spatial information or archaeological artifacts. This extends to knowledge graphs. In this paper, we will summarize, compare and evaluate existing and novel methods to analyze the robustness of networks. We introduce a method with different removal strategies to analyze how additional or missing layers, nodes, or edges in a random network influence centrality measures. We can show that the robustness of social networks regardless of the error measures heavily relies on network structures, which brings up several new challenges for future research. In general, we may assume networks to be rather robust against small errors and few missing data if they follow a scale-free distribution. The results of this paper are not limited to social networks but can be applied in all fields working with centrality measures.</div></div>\",\"PeriodicalId\":74826,\"journal\":{\"name\":\"Social sciences & humanities open\",\"volume\":\"11 \",\"pages\":\"Article 101183\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Social sciences & humanities open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590291124003802\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social sciences & humanities open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590291124003802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

中心性测量首先是在社会网络分析(SNA)中引入的,它被广泛应用于人文和社会科学。然而,最近它们在网络科学中得到了广泛的关注,例如知识图和人工智能。假设网络不是没有错误或包含多层,我们可以识别其质量的挑战。例如,古代语言的历史和叙事文本由于其复杂性和缺乏模型,通常对为现代语言开发的自然语言处理方法和人工智能技术具有挑战性。此外,由于安静源,网络中的节点和边可能会缺失,这可能会影响SNA的结果。其他方面也可能对数据产生影响,因为许多网络包括额外的层,例如空间信息或考古文物。这延伸到知识图。在本文中,我们将总结,比较和评估现有的和新的方法来分析网络的鲁棒性。我们引入了一种具有不同移除策略的方法来分析随机网络中额外或缺失的层、节点或边如何影响中心性度量。我们可以证明,无论误差测量如何,社交网络的鲁棒性在很大程度上依赖于网络结构,这给未来的研究带来了几个新的挑战。一般来说,如果网络遵循无标度分布,我们可以假设网络对小错误和很少丢失的数据具有相当强的鲁棒性。本文的结果不仅限于社交网络,而且可以应用于使用中心性度量的所有领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach towards the robustness of centrality measures in networks
Centrality measures were first introduced in the Social Network Analysis (SNA), which is widely used in the humanities and social sciences. However, recently they are receiving attention in network science in general, e.g., for knowledge graphs and artificial intelligence. Assuming that the network is not error-free or contains multiple layers, we can identify challenges for their quality. For example, historical and narrative texts in ancient languages are usually challenging for natural language processing methods and artificial intelligence technologies developed for modern languages due to their complexity and missing models. In addition, due to quiet sources, nodes and edges in the network may be missing, which might influence the results of SNA. Other aspects may also have an effect on data, as many networks include additional layers, e.g. spatial information or archaeological artifacts. This extends to knowledge graphs. In this paper, we will summarize, compare and evaluate existing and novel methods to analyze the robustness of networks. We introduce a method with different removal strategies to analyze how additional or missing layers, nodes, or edges in a random network influence centrality measures. We can show that the robustness of social networks regardless of the error measures heavily relies on network structures, which brings up several new challenges for future research. In general, we may assume networks to be rather robust against small errors and few missing data if they follow a scale-free distribution. The results of this paper are not limited to social networks but can be applied in all fields working with centrality measures.
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
Social sciences & humanities open
Social sciences & humanities open Psychology (General), Decision Sciences (General), Social Sciences (General)
CiteScore
4.20
自引率
0.00%
发文量
0
审稿时长
159 days
×
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