BibSonomy publications for /user/hotho/graphWed Jun 04 11:45:49 CEST 2008New York, NY, USAICML '05: Proceedings of the 22nd international conference on Machine learning41--48Multi-way distributional clustering via pairwise interactions2005coclustering clustering hierarchical graph multi multiway Sat Apr 26 12:29:32 CEST 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining717--726A framework for community identification in dynamic social networks2007graph clustering toread detection community Thu Apr 24 09:02:43 CEST 2008SIAM International Conference on Data MiningR-MAT: A Recursive Model for Graph Mining2004toread mining model graph Tue Jan 15 10:33:14 CET 2008Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJune530-544LNCSContent Aggregation on Knowledge Bases using Graph Clustering40112006content ontology theory graph 2006 clustering myown aggregation Sun Oct 28 22:18:15 CET 2007ACM Southeast Regional Conferenceconf/ACMse/2006280-285Discovering communities in complex networks.2006soical graph clustering dm networks communities toread Thu Jun 21 08:22:03 CEST 2007Kernels for structured data2002kernels toread graph Thu May 10 00:12:21 CEST 2007Proc of the wwwwExtraction and Classification of Dense Communities in the WebAuthors2007www clustering graph 2007 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.Wed May 02 16:13:52 CEST 2007Los Alamitos, CA, USAwi121-128WMR--A Graph-Based Algorithm for Friend Recommendation02006graph recommender Wed May 02 16:09:48 CEST 2007JCDL '0265--73A Graph-based Recommender System for Digital Library2002graph recommender systems Wed Apr 04 10:13:10 CEST 2007Document Analysis Systemsconf/das/2004401-412Lecture Notes in Computer ScienceA Graph-Based Framework for Web Document Mining.31632004toread graph based mining web Wed Apr 04 10:12:35 CEST 2007Graph-Theoretic Techniques for Web Content Mining2005graph clustering web mining toread Tue Mar 13 17:55:17 CET 2007Comparative Graph Theoretical Characterization of Networks of Spam and Legitimate Email2005spam email graph network Email is an increasingly important and ubiquitous means of communication, both facilitating contact between private individuals and enabling rises in the productivity of organizations. However the relentless rise of automatic unauthorized emails, a.k.a. spam is eroding away much of the attractiveness of email communication. Most of the attention dedicated to date to spam detection has focused on the content of the emails or on the addresses or domains associated with spam senders. Although methods based on these - easily changeable - identifiers work reasonably well they miss on the fundamental nature of spam as an opportunistic relationship, very different from the normal mutual relations between senders and recipients of legitimate email. Here we present a comprehensive graph theoretical analysis of email traffic that captures these properties quantitatively. We identify several simple metrics that serve both to distinguish between spam and legitimate email and to provide a statistical basis for models of spam traffic.Wed Feb 28 11:17:10 CET 2007KDDGraphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations2005time graph toread Mon Feb 12 21:48:01 CET 2007New York, NY, USASIGKDD Explorations159--68State of the art of graph-based data mining52003graph dm toread The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well. Tue Jan 23 15:32:46 CET 2007FebDefining and identifying communities in networks2004graph clustering folksonomy toread community The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic, cellular or protein networks) or technological problems (optimization of large infrastructures). Several types of algorithm exist for revealing the community structure in networks, but a general and quantitative definition of community is still lacking, leading to an intrinsic difficulty in the interpretation of the results of the algorithms without any additional non-topological information. In this paper we face this problem by introducing two quantitative definitions of community and by showing how they are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a new local algorithm to detect communities which outperforms the existing algorithms with respect to the computational cost, keeping the same level of reliability. The new algorithm is tested on artificial and real-world graphs. In particular we show the application of the new algorithm to a network of scientific collaborations, which, for its size, can not be attacked with the usual methods. This new class of local algorithms could open the way to applications to large-scale technological and biological applications.Mon Sep 11 11:30:56 CEST 2006New York, NY, USASIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval308--315Generalizing PageRank: damping functions for link-based ranking algorithms2006graph pagerank rank toread Mon Sep 11 11:28:15 CEST 2006New York, NY, USASIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval75--82AggregateRank: bringing order to web sites2006toread graph rank Mon May 15 18:13:11 CEST 2006ECAI440-444PoBOC: An Overlapping Clustering Algorithm, Application to Rule-Based Classification and Textual Data.2004clustering PoBOC graph Tue Jan 31 16:53:45 CET 2006CairoProceedings of the 26th International Conference onVery Large Databases (VLDB)September527--534Focused crawling using context graphs2000focused crawling graph Tue Dec 20 20:21:42 CET 2005Conceptual Structures: Logical, Linguistic, and Computational Issues, 8th International Conference on Conceptual Structures, ICCS 2000, Darmstadt, Germany, August 14-18, 2000, Proceedings468--482LNCSA conceptual graph model for W3C resource description framework18672000framework conceptual resource model description graph