BibSonomy publications for /tag/linkFri Apr 25 12:21:04 CEST 2008Proceedings of the National Academy of Sciences85207-5211Winners don't take all: Characterizing the competition for links on the web992002Link analysis Wed Apr 23 22:05:04 CEST 2008Statistics \& Probability LettersFeb4379--382Analysis of goodness-of-fit for Cox regression model411999Log-log link 15Wed Apr 23 22:05:04 CEST 2008Journal of Statistical Planning and InferenceJan11--12Pairwise conditional score functions: a generalization of the Mantel-Haenszel estimator1282005Canonical function link 15Wed Apr 23 14:55:19 CEST 2008Computational Statistics \& Data AnalysisMar113--17The lower bound method in probit regression301999regression link and probit Probit function 28Wed Apr 23 14:55:19 CEST 2008Computational Statistics \& Data AnalysisOct1-2409--418Web-based statistical system by using the DLL442003library Dynamic (DLL) link 28Wed Apr 23 14:55:19 CEST 2008Computational Statistics \& Data AnalysisAug126565--6581Testing the link when the index is semiparametric--a comparative study512007Testing the link 15Mon Apr 21 22:09:52 CEST 2008Economics LettersApr195--101Reciprocity, matching and conditional cooperation in two public goods games872005Weakest link mechanism Fri Apr 18 11:45:18 CEST 2008IJCAIconf/ijcai/2001903-910Link Analysis, Eigenvectors and Stability.2001link-analysis link eigenvectors eigenvalues Thu Mar 27 19:22:41 CET 2008Search Engines, Link Analysis, and User's Web Behavior2008engines Information Link retrieval user behavior analysis search Mon Nov 12 10:42:39 CET 2007Proceedings of the Workshop on Finding Experts on the Web with Semantics (FEWS2007) at ISWC/ASWC2007, Busan, South Koreahttp://data.semanticweb.org/workshop/fews/2007/proceedingsNovemberFinding Experts by Link Prediction in Co-authorship Networks2007Networks finding Link expert link authorship Research collaborations are always encouraged, as they often yield good results. However, the researcher network contains massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best. As a result, collaboration outcomes are often uncertain and research teams are poorly organized. We propose a method for building link predictors in networks, where nodes can represent researchers and links - collaborations. In this case, predictors might offer good suggestions for future collaborations. We test our method on a researcher co-authorship network and obtain link predictors of encouraging accuracy. This leads us to believe our method could be useful in building and maintaining strong research teams. It could also help with choosing vocabulary for expert description, since link predictors contain implicit information about which structural attributes of the network are important with respect to the link prediction problem.Wed Nov 07 19:17:54 CET 2007Proceedings of the Workshop on Finding Experts on the Web with Semantics (FEWS2007) at ISWC/ASWC2007, Busan, South Koreahttp://data.semanticweb.org/workshop/fews/2007/proceedingsNovemberFinding Experts by Link Prediction in Co-authorship Networks20072007 iswc finding expert prediction network workshop_fews link Research collaborations are always encouraged, as they often yield good results. However, the researcher network contains massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best. As a result, collaboration outcomes are often uncertain and research teams are poorly organized. We propose a method for building link predictors in networks, where nodes can represent researchers and links - collaborations. In this case, predictors might offer good suggestions for future collaborations. We test our method on a researcher co-authorship network and obtain link predictors of encouraging accuracy. This leads us to believe our method could be useful in building and maintaining strong research teams. It could also help with choosing vocabulary for expert description, since link predictors contain implicit information about which structural attributes of the network are important with respect to the link prediction problem.Sat Nov 03 00:18:07 CET 2007New York, NY, USASIGKDD Explor. Newsl.223--30Prediction and ranking algorithms for event-based network data72005link sna mining temporal Wed Aug 15 12:04:46 CEST 2007Seattle, WAKDD Workshop on Link Analysis and Group DetectionAugustDeduplication and Group Detection using Links2004group link Wed Aug 15 12:04:30 CEST 2007Seattle, WAIJCAI Workshop on "Text Learning: Beyond Supervision"AugustProbabilistic Models of Text and Link Structure for Hypertext Classification2001ml hypertext link classification learning probabilistic Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated. For example, web pages of related topics are often connected by hyperlinks and scientific papers from related fields are commonly linked by citations. We propose a unified probabilistic model for both the textual content and the link structure of a document collection. Our model is based on the recently introduced framework of Probabilistic Relational Models (PRMs), which allows us to capture correlations between linked documents. We show how to learn these models from data and use them efficiently for classification. Since exact methods for classification in these large models are intractable, we utilize belief propagation, an approximate inference algorithm. Belief propagation automatically induces a very natural behavior, where our knowledge about one document helps us classify related ones, which in turn help us classify others. We present preliminary empirical results on a dataset of university web pages.Wed Aug 15 12:04:07 CEST 2007Cambridge, MA, USAJournal of Machine Learning ResearchDecember679--707Learning probabilistic models of link structure32002link learning ml probabilistic Sat Jul 14 10:58:22 CEST 2007JulyIn KDD'02Efficiently mining frequent trees in a forest2002mining link Sat Jul 14 10:58:22 CEST 2007SIGKDD Explorations276-83Link mining applications: progress and challenges.72005link mining Sat Jul 14 10:58:22 CEST 2007New York, NY, USASIGKDD Explor. Newsl.23--12Link mining: a survey72005mining link Sat Jul 14 10:58:22 CEST 2007New York, NY, USASIGKDD Explor. Newsl.184--89Link mining: a new data mining challenge52003link mining linkCommunity for tag(s) link