{"title":"大型流和序列图的平滑捆绑","authors":"C. Hurter, O. Ersoy, A. Telea","doi":"10.1109/PacificVis.2013.6596126","DOIUrl":null,"url":null,"abstract":"Dynamic graphs are increasingly pervasive in modern information systems. However, understanding how a graph changes in time is difficult. We present here two techniques for simplified visualization of dynamic graphs using edge bundles. The first technique uses a recent image-based graph bundling method to create smoothly changing bundles from streaming graphs. The second technique incorporates additional edge-correspondence data and is thereby suited to visualize discrete graph sequences. We illustrate our methods with examples from real-world large dynamic graph datasets.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"Smooth bundling of large streaming and sequence graphs\",\"authors\":\"C. Hurter, O. Ersoy, A. Telea\",\"doi\":\"10.1109/PacificVis.2013.6596126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dynamic graphs are increasingly pervasive in modern information systems. However, understanding how a graph changes in time is difficult. We present here two techniques for simplified visualization of dynamic graphs using edge bundles. The first technique uses a recent image-based graph bundling method to create smoothly changing bundles from streaming graphs. The second technique incorporates additional edge-correspondence data and is thereby suited to visualize discrete graph sequences. We illustrate our methods with examples from real-world large dynamic graph datasets.\",\"PeriodicalId\":179865,\"journal\":{\"name\":\"2013 IEEE Pacific Visualization Symposium (PacificVis)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Pacific Visualization Symposium (PacificVis)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PacificVis.2013.6596126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Pacific Visualization Symposium (PacificVis)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PacificVis.2013.6596126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smooth bundling of large streaming and sequence graphs
Dynamic graphs are increasingly pervasive in modern information systems. However, understanding how a graph changes in time is difficult. We present here two techniques for simplified visualization of dynamic graphs using edge bundles. The first technique uses a recent image-based graph bundling method to create smoothly changing bundles from streaming graphs. The second technique incorporates additional edge-correspondence data and is thereby suited to visualize discrete graph sequences. We illustrate our methods with examples from real-world large dynamic graph datasets.