{"title":"现实世界中的密集图有多密集?方法说明","authors":"G. Melançon","doi":"10.1145/1168149.1168167","DOIUrl":null,"url":null,"abstract":"This methodological note focuses on the edge density of real world examples of networks. The edge density is a parameter of interest typically when putting up user studies in an effort to prove the robustness or superiority of a novel graph visualization technique. We survey many real world examples all being of equal interest in Information Visualization, and draw a list of conclusions on how to tune edge density when randomly generating graphs in order to build artificial though realistic examples.","PeriodicalId":235801,"journal":{"name":"Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":"{\"title\":\"Just how dense are dense graphs in the real world?: a methodological note\",\"authors\":\"G. Melançon\",\"doi\":\"10.1145/1168149.1168167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This methodological note focuses on the edge density of real world examples of networks. The edge density is a parameter of interest typically when putting up user studies in an effort to prove the robustness or superiority of a novel graph visualization technique. We survey many real world examples all being of equal interest in Information Visualization, and draw a list of conclusions on how to tune edge density when randomly generating graphs in order to build artificial though realistic examples.\",\"PeriodicalId\":235801,\"journal\":{\"name\":\"Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"69\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1168149.1168167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1168149.1168167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Just how dense are dense graphs in the real world?: a methodological note
This methodological note focuses on the edge density of real world examples of networks. The edge density is a parameter of interest typically when putting up user studies in an effort to prove the robustness or superiority of a novel graph visualization technique. We survey many real world examples all being of equal interest in Information Visualization, and draw a list of conclusions on how to tune edge density when randomly generating graphs in order to build artificial though realistic examples.