{"title":"用Tulip探索InfoVis的出版历史","authors":"M. Delest, T. Munzner, D. Auber, J. Domenger","doi":"10.1109/INFVIS.2004.23","DOIUrl":null,"url":null,"abstract":"We show the structure of the InfoVis publications dataset using Tulip, a scalable open-source visualization system for graphs and trees. Tulip supports interactive navigation and many options for layout. Subgraphs of the full dataset can be created interactively or using a wide set of algorithms based on graph theory and combinatorics, including several kinds of clustering. We found that convolution clustering and small world clustering were particularly effective at showing the structure of the InfoVis publications dataset, as was coloring by the Strahler metric.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"273 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Exploring InfoVis Publication History with Tulip\",\"authors\":\"M. Delest, T. Munzner, D. Auber, J. Domenger\",\"doi\":\"10.1109/INFVIS.2004.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show the structure of the InfoVis publications dataset using Tulip, a scalable open-source visualization system for graphs and trees. Tulip supports interactive navigation and many options for layout. Subgraphs of the full dataset can be created interactively or using a wide set of algorithms based on graph theory and combinatorics, including several kinds of clustering. We found that convolution clustering and small world clustering were particularly effective at showing the structure of the InfoVis publications dataset, as was coloring by the Strahler metric.\",\"PeriodicalId\":109217,\"journal\":{\"name\":\"IEEE Symposium on Information Visualization\",\"volume\":\"273 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Symposium on Information Visualization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFVIS.2004.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Symposium on Information Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFVIS.2004.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We show the structure of the InfoVis publications dataset using Tulip, a scalable open-source visualization system for graphs and trees. Tulip supports interactive navigation and many options for layout. Subgraphs of the full dataset can be created interactively or using a wide set of algorithms based on graph theory and combinatorics, including several kinds of clustering. We found that convolution clustering and small world clustering were particularly effective at showing the structure of the InfoVis publications dataset, as was coloring by the Strahler metric.