http://www.bibsonomy.org/burst/user/hotho/wwwBibSonomy BuRST Feed for /user/hotho/www2008-10-07T18:40:41+02:00WWW2007 Paper Detailshttp://www.bibsonomy.org/bibtex/2480a63c3e6847dc8a9ebd3de040501db/hothohotho2007-05-10T00:12:21+02:00graph clustering 2007 www <span style="color:#555555;">Yon <a href="http://www.bibsonomy.org/author/Dourisboure">Dourisboure</a> und Filippo <a href="http://www.bibsonomy.org/author/Geraci">Geraci</a> und Marco <a href="http://www.bibsonomy.org/author/Pellegrini">Pellegrini</a> </span><em>Proc of the wwww, </em>(<em>2007</em>)Thu May 10 00:12:21 CEST 2007Proc of the wwwwExtraction and Classification of Dense Communities in the WebAuthors2007graph clustering 2007 www The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying {\em communities} in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100\% for communities of a thirty nodes or more (even at low density). It is still about 80\% even for communities of twenty nodes with density over $50\%$ of the arcs present. At the lower extremes the algorithm catches 35\% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.http://www.bibsonomy.org/bibtex/27d3c70d55c118425216a7375f749c2f2/hothohotho2006-01-10T13:55:26+01:00find www page search <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Dean">Dean</a> und M.R. <a href="http://www.bibsonomy.org/author/Henzinger">Henzinger</a> </span><em>Proceedings of the Eighth International World Wide Web Conference WWW-1999, </em><em>Toronto, </em><em>May1999. </em>Tue Jan 10 13:55:26 CET 2006TorontoProceedings of the Eighth International World Wide Web Conference WWW-1999MayFinding related pages in the World Wide Web1999find www page search http://www.bibsonomy.org/bibtex/2e515dc2a8adbc7fa84b7fe968b61391e/hothohotho2006-01-08T11:47:30+01:00mining pattern preparation data browsing www <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Cooley">Cooley</a> und B. <a href="http://www.bibsonomy.org/author/Mobasher">Mobasher</a> und J. <a href="http://www.bibsonomy.org/author/Srivastava">Srivastava</a> </span><em>Journal of Knowledge and Information Systems</em><em>1(1):5--32</em>(<em>1999</em>)Sun Jan 08 11:47:30 CET 2006Journal of Knowledge and Information Systems15--32Data preparation for mining world wide web browsing patterns11999mining pattern preparation data browsing www