Xiaodi Huang, W. Lai, Di Zhang, M. Huang, Quang Vinh Nguyen
{"title":"A Kernel-based Algorithm for Multilevel Drawing Web Graphs","authors":"Xiaodi Huang, W. Lai, Di Zhang, M. Huang, Quang Vinh Nguyen","doi":"10.1109/CGIV.2007.7","DOIUrl":null,"url":null,"abstract":"A Web graph refers to the graph that models the hyperlink relations between Web pages in the WWW, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is normally a very huge graph. In the course of users' Web exploration, only part of the Web graph is displayed on the screen each time according to a user's current navigation focus. In this paper, we make use of a fast kernel-based algorithm that is able to cluster large graphs. The algorithm is implemented in an online visualization system of Web graphs. In the system, a Web crawler first generates the Web graph of web sites. The clustering algorithm then reduces the visual complexities of the large graph. In particular, it groups a set of highly connected nodes and their edges into a clustered graph with abstract nodes and edges. The experiments have demonstrated that the employed algorithm is able to cluster graphs.","PeriodicalId":433577,"journal":{"name":"Computer Graphics, Imaging and Visualisation (CGIV 2007)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Graphics, Imaging and Visualisation (CGIV 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGIV.2007.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A Web graph refers to the graph that models the hyperlink relations between Web pages in the WWW, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is normally a very huge graph. In the course of users' Web exploration, only part of the Web graph is displayed on the screen each time according to a user's current navigation focus. In this paper, we make use of a fast kernel-based algorithm that is able to cluster large graphs. The algorithm is implemented in an online visualization system of Web graphs. In the system, a Web crawler first generates the Web graph of web sites. The clustering algorithm then reduces the visual complexities of the large graph. In particular, it groups a set of highly connected nodes and their edges into a clustered graph with abstract nodes and edges. The experiments have demonstrated that the employed algorithm is able to cluster graphs.