{"title":"Comparing Graph Clusterings: Set Partition Measures vs. Graph-Aware Measures.","authors":"Valerie Poulin, Francois Theberge","doi":"10.1109/TPAMI.2020.3009862","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graphs. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to compare graph partitions.</p>","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"43 6","pages":"2127-2132"},"PeriodicalIF":20.8000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TPAMI.2020.3009862","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Pattern Analysis and Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/TPAMI.2020.3009862","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/5/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graphs. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to compare graph partitions.
期刊介绍：
The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.
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