BibSonomyburst/group/iteg/analysisBibSonomy RSS feed for /group/iteg/analysis2012-02-16T10:45:44+01:00http://www.bibsonomy.org/bibtex/2faf63fce8153a9948078f90916ba3994/dbenzdbenz2012-02-16T07:56:51+01:00analysis decision spreadsheet <span class="authorEditorList"><a href="/author/Ragsdale">C.T. Ragsdale</a> </span><em>Cincinnati, OH: South-Western College Publishing</em> (<em>2008</em>)Thu Feb 16 07:56:51 CET 2012Cincinnati, OH: South-Western College PublishingSpreadsheet Modeling \& Decision Analysis 5e2008analysis decision spreadsheet http://www.bibsonomy.org/bibtex/277269acabb5772a6fa8f7cb09a8bc1f4/folkefolke2012-02-15T15:42:09+01:00analysis clustering network sna transitivity <span class="authorEditorList"><a href="/author/Schank">T. Schank</a>, and <a href="/author/Wagner">D. Wagner</a> </span><em>Universität Karlsruhe, Fakultät für Informatik, </em>(<em>2004</em>)Wed Feb 15 15:42:09 CET 2012Approximating clustering-coefficient and transitivity2004analysis clustering network sna transitivity http://www.bibsonomy.org/bibtex/28fc929ccef18d4c968127da91ce35ae8/folkefolke2012-02-15T15:38:40+01:00analysis bipartite network twomode <span class="authorEditorList"><a href="/author/Latapy">M. Latapy</a>, <a href="/author/Magnien">C. Magnien</a>, and <a href="/author/Vecchio">N.D. Vecchio</a> </span><em>Social Networks</em> <em>30(1):31--48</em> (<em>2008</em>)Wed Feb 15 15:38:40 CET 2012Social Networks131--48Basic notions for the analysis of large two-mode networks302008analysis bipartite network twomode http://www.bibsonomy.org/bibtex/2a326012bd3b8dd805aafaa79e7d5742b/folkefolke2012-02-15T15:36:46+01:00analysis network sna weighted <span class="authorEditorList"><a href="/author/Barrat">A. Barrat</a>, <a href="/author/Barthélemy">M. Barthélemy</a>, <a href="/author/Pastor-Satorras">R. Pastor-Satorras</a>, and <a href="/author/Vespignani">A. Vespignani</a> </span><em>Proceedings of the National Academy of Sciences of the United States of America</em> <em>101(11):3747-3752</em> (<em>2004</em>)Wed Feb 15 15:36:46 CET 2012Proceedings of the National Academy of Sciences of the United States of America113747-3752The architecture of complex weighted networks1012004analysis network sna weighted Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.The architecture of complex weighted networkshttp://www.bibsonomy.org/bibtex/2d3b63acbea4994ce06e347c6b935fb9b/folkefolke2012-02-15T15:29:03+01:00analysis clustering network transitivity weighted <span class="authorEditorList"><a href="/author/Saramäki">J. Saramäki</a>, <a href="/author/Kivelä">M. Kivelä</a>, <a href="/author/Onnela">J.P. Onnela</a>, <a href="/author/Kaski">K. Kaski</a>, and <a href="/author/Kertesz">J. Kertesz</a> </span><em>Physical Review E</em> <em>75(2):027105</em> (<em>2007</em>)Wed Feb 15 15:29:03 CET 2012Physical Review E2027105Generalizations of the clustering coefficient to weighted complex networks752007analysis clustering network transitivity weighted http://www.bibsonomy.org/bibtex/2268e9546446d8bd65e0a15792879e333/folkefolke2012-02-14T00:56:20+01:00analysis entropy graph network theory <span class="authorEditorList"><a href="/author/Simonyi">G. Simonyi</a> </span><em>Combinatorial Optimization</em> (<em>1995</em>)Tue Feb 14 00:56:20 CET 2012Combinatorial Optimization399--441Graph entropy: A survey201995analysis entropy graph network theory http://www.bibsonomy.org/bibtex/27f9e0de989ed9bd4deaa41d39bc98d4c/folkefolke2012-02-14T00:53:13+01:00analysis entropy graph network theory <span class="authorEditorList"><a href="/author/Dehmer">M. Dehmer</a> </span><em>Applied Mathematics and Computation</em> <em>201(1):82--94</em> (<em>2008</em>)Tue Feb 14 00:53:13 CET 2012Applied Mathematics and Computation182--94Information processing in complex networks: Graph entropy and information functionals2012008analysis entropy graph network theory http://www.bibsonomy.org/bibtex/2d169c08d5241a0912f3d60c97d87e2c0/hothohotho2012-02-12T10:50:22+01:00analysis emotions sentiment toread <span class="authorEditorList"><a href="/author/Kim">Seungyeon Kim</a>, <a href="/author/Li">Fuxin Li</a>, <a href="/author/Lebanon">Guy Lebanon</a>, and <a href="/author/Essa">Irfan Essa</a> </span>(<em>2012</em>)<em>cite arxiv:1202.1568Comment: 15 pages, 7 figures . </em>Sun Feb 12 10:50:22 CET 2012cite arxiv:1202.1568Comment: 15 pages, 7 figuresBeyond Sentiment: The Manifold of Human Emotions2012analysis emotions sentiment toread Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains. Beyond Sentiment: The Manifold of Human Emotionshttp://www.bibsonomy.org/bibtex/289d31bda2a1c792d8afa4fe438f90873/folkefolke2012-02-10T10:51:58+01:00analysis android market <span class="authorEditorList"><a href="/author/Girardello">Andrea Girardello</a>, and <a href="/author/Michahelles">Florian Michahelles</a> </span><em>Proceedings of the 12th international conference on Human computer interaction with mobile devices and services, </em><em>page 431--434. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Fri Feb 10 10:51:58 CET 2012New York, NY, USAProceedings of the 12th international conference on Human computer interaction with mobile devices and services431--434MobileHCI '10AppAware: which mobile applications are hot?2010analysis android market Today most mobile operating systems provide users with an application portal where they can search for applications published by third-party developers. However, finding new apps is not an easy task and requires either to know what to look for or to go through an endless list of applications. In this paper we present work in progress of a platform that allows its users to discover mobile applications in a serendipitous manner. AppAware is a mobile application that captures and shares installations, updates, and removals of Android programs in real time. Accordingly, AppAware allows its users to see what applications are being installed right now or around their position by other people, thus introducing a new way of interaction with application portals and other mobile users.AppAwarehttp://www.bibsonomy.org/bibtex/20834c85062338a7da4a014923467c2ca/folkefolke2012-02-06T15:57:57+01:00analysis entropy folksonomy <span class="authorEditorList"><a href="/author/Eda">Takeharu Eda</a>, <a href="/author/Yoshikawa">Masatoshi Yoshikawa</a>, <a href="/author/Uchiyama">Toshio Uchiyama</a>, and <a href="/author/Uchiyama">Tadasu Uchiyama</a> </span><em>World Wide Web</em> (<em>December 2009</em>)Mon Feb 06 15:57:57 CET 2012Hingham, MA, USAWorld Wide WebDecember421--440The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags122009analysis entropy folksonomy In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like "cool" and "fun" from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tagshttp://www.bibsonomy.org/bibtex/2d44d1c9a48f5b676388ffbc90c7577ba/folkefolke2012-02-06T15:42:31+01:00analysis entropy folksonomy tagging <span class="authorEditorList"><a href="/author/Chi">Ed H. Chi</a>, and <a href="/author/Mytkowicz">Todd Mytkowicz</a> </span><em>Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page 81--88. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Mon Feb 06 15:42:31 CET 2012New York, NY, USAProceedings of the nineteenth ACM conference on Hypertext and hypermedia81--88HT '08Understanding the efficiency of social tagging systems using information theory2008analysis entropy folksonomy tagging Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.Understanding the efficiency of social tagging systems using information theoryhttp://www.bibsonomy.org/bibtex/24cf42ebabd9a670c70bee456affda285/hothohotho2012-01-27T14:02:47+01:002011 analysis data media myown social ubiquitous <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Strohmaier">Markus Strohmaier</a>, and <a href="/author/Chin">Alvin Chin</a> (Eds.). </span><em>volume 6904 of Lecture Notes in Computer Science, </em><em>Springer, </em>(<em>2011</em>)Fri Jan 27 14:02:47 CET 2012MSM/MUSELecture Notes in Computer ScienceAnalysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers690420112011 analysis data media myown social ubiquitous http://www.bibsonomy.org/bibtex/24cf42ebabd9a670c70bee456affda285/atzmuelleratzmueller2012-01-27T10:51:13+01:002011 analysis data itegpub media myown social ubiquitous <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Strohmaier">Markus Strohmaier</a>, and <a href="/author/Chin">Alvin Chin</a> (Eds.). </span><em>volume 6904 of Lecture Notes in Computer Science, </em><em>Springer, </em>(<em>2011</em>)Fri Jan 27 10:51:13 CET 2012MSM/MUSELecture Notes in Computer ScienceAnalysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers690420112011 analysis data itegpub media myown social ubiquitous http://www.bibsonomy.org/bibtex/284840e0e94320b5742df381f2ec033b7/stummestumme2012-01-04T11:24:21+01:002011 LWA analysis conferator contacts itegpub myown <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 04 11:24:21 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationFace-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players20112011 LWA analysis conferator contacts itegpub myown http://www.bibsonomy.org/bibtex/21fe037ea2712b205c564243d67840059/stummestumme2012-01-04T11:23:25+01:002011 analysis communities community conferator discovery itegpub knowledge myown rfid <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proc. Workshop on Mining Ubiquitous and Social Environments MUSE 2011 at ECML/PKDD 2011, </em>(<em>2011</em>)Wed Jan 04 11:23:25 CET 2012Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players20112011 analysis communities community conferator discovery itegpub knowledge myown rfid http://www.bibsonomy.org/bibtex/2382c8a20204cca36d1843ddd2f8288d8/folkefolke2011-12-08T11:18:23+01:00analysis carlo hypothesis model monte network null sna statistics <span class="authorEditorList"><a href="/author/Snijders">Tom Snijders</a> </span><em>Psychometrika</em> (<em>1991</em>)Thu Dec 08 11:18:23 CET 2011Psychometrika397-417Enumeration and simulation methods for 0–1 matrices with given marginals561991analysis carlo hypothesis model monte network null sna statistics SpringerLink - Psychometrika, Volume 56, Number 3http://www.bibsonomy.org/bibtex/2f66f8994a84b6c45891d428fb5ed28be/folkefolke2011-12-08T11:16:02+01:00analysis introduction network review sna survey <span class="authorEditorList"><a href="/author/Albert">R. Albert</a>, and <a href="/author/Barabási">A.L. Barabási</a> </span><em>Reviews of modern physics</em> <em>74(1):47</em> (<em>2002</em>)Thu Dec 08 11:16:02 CET 2011Reviews of modern physics147Statistical mechanics of complex networks742002analysis introduction network review sna survey http://www.bibsonomy.org/bibtex/24fd1893c98176e0c7ec855b72f813c30/folkefolke2011-12-08T11:10:59+01:00analysis degree distribution network sna <span class="authorEditorList"><a href="/author/Rapoport">Anatol Rapoport</a> </span><em>Bulletin of Mathematical Biology</em> (<em>1957</em>)Thu Dec 08 11:10:59 CET 2011Bulletin of Mathematical Biology257-277Contribution to the theory of random and biased nets191957analysis degree distribution network sna The probabilistic theory of random and biased nets is further developed by the “tracing” method treated previously. A number of biases expected to be operating in nets, particularly in sociograms, is described. Distribution of closed chain lengths is derived for random nets and for nets with a simple “reflexive” bias. The “island model” bias is treated for the case of two islands and a single axon tracing, resulting in a pair of linear difference equations with two indices. The reflexive bias is extended to multiple-axon tracing by an approximate method resulting in a modification of the random net recursion formula. Results previously obtained are compared with empirical findings and attempts are made to account for observed discrepancies.SpringerLink - Bulletin of Mathematical Biology, Volume 19, Number 4http://www.bibsonomy.org/bibtex/2e49e8d01a18c1d98dabda18b1024bcc8/folkefolke2011-12-08T11:08:21+01:00analysis degree distribution network sna <span class="authorEditorList"><a href="/author/Snijders">T.A.B. Snijders</a> </span><em>Dynamic Social Network Modeling and Analysis: workshop summary and papers, </em><em>page 146--161. </em>(<em>2003</em>)Thu Dec 08 11:08:21 CET 2011Dynamic Social Network Modeling and Analysis: workshop summary and papers146--161Accounting for degree distributions in empirical analysis of network dynamics2003analysis degree distribution network sna http://www.bibsonomy.org/bibtex/21fe037ea2712b205c564243d67840059/hothohotho2011-11-30T14:54:07+01:002011 analysis communities community discovery knowledge myown rfid <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proc. Workshop on Mining Ubiquitous and Social Environments MUSE 2011 at ECML/PKDD 2011, </em>(<em>2011</em>)Wed Nov 30 14:54:07 CET 2011Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players20112011 analysis communities community discovery knowledge myown rfid