@article{liu2012fulltext, author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun}, interhash = {011df26355ad51a88947017fd2791a98}, intrahash = {f9c6133bf4503003822f99860f864698}, journal = {Journal of the American Society for Information Science and Technology}, title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network}, url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf}, year = 2012 } @article{liu2012fulltext, author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun}, interhash = {011df26355ad51a88947017fd2791a98}, intrahash = {f9c6133bf4503003822f99860f864698}, journal = {Journal of the American Society for Information Science and Technology}, title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network}, url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf}, year = 2012 } @inproceedings{hotho2006information, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies,called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to findcommunities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.}, address = {Heidelberg}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, file = {hotho2006information.pdf:hotho2006information.pdf:PDF}, groups = {public}, interhash = {10ec64d80b0ac085328a953bb494fb89}, intrahash = {3c301945817681d637ee43901c016939}, month = {June}, pages = {411-426}, pdf = {hotho2006information.pdf}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2010-11-11 15:34:51}, title = {Information Retrieval in Folksonomies: Search and Ranking}, username = {dbenz}, volume = 4011, year = 2006 } @article{johannes2009binary, abstract = {Bipartite ranking refers to the problem of learning a ranking function from a training set of positively and negatively labeled examples. Applied to a set of unlabeled instances, a ranking function is expected to establish a total order in which positiveinstances precede negative ones. The performance of a ranking function is typically measured in terms of the AUC. In thispaper, we study the problem of multipartite ranking, an extension of bipartite ranking to the multi-class case. In this regard,we discuss extensions of the AUC metric which are suitable as evaluation criteria for multipartite rankings. Moreover, tolearn multipartite ranking functions, we propose methods on the basis of binary decomposition techniques that have previouslybeen used for multi-class and ordinal classification. We compare these methods both analytically and experimentally, not onlyagainst each other but also to existing methods applicable to the same problem.}, author = {Fürnkranz, Johannes and Hüllermeier, Eyke and Vanderlooy, Stijn}, interhash = {780e00a583e280eebfa4d87cc74e62a1}, intrahash = {472363e85298f4d6e188d92a4319918a}, journal = {Machine Learning and Knowledge Discovery in Databases}, pages = {359--374}, title = {Binary Decomposition Methods for Multipartite Ranking}, url = {http://dx.doi.org/10.1007/978-3-642-04180-8_41}, year = 2009 } @inproceedings{hotho2006information, author = {Hotho, Andreas and J{\"a}schke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, interhash = {882bd942131c6c303bdc9c4732287ae9}, intrahash = {087d10ce5603cef6c6a8b443700368a2}, pages = {411-426}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Information Retrieval in Folksonomies: Search and Ranking}, year = 2006 } @inproceedings{conf/ht/WuZM06, author = {Wu, Harris and Zubair, Mohammad and Maly, Kurt}, booktitle = {Hypertext}, crossref = {conf/ht/2006}, date = {2006-09-28}, editor = {Wiil, Uffe Kock and Nürnberg, Peter J. and Rubart, Jessica}, ee = {http://doi.acm.org/10.1145/1149941.1149962}, interhash = {ea6aa5db3724812d08347d5a8309bea4}, intrahash = {4b0512091911843390f88699d3ea3bb9}, isbn = {1-59593-417-0}, pages = {111-114}, publisher = {ACM}, title = {Harvesting social knowledge from folksonomies.}, url = {http://dblp.uni-trier.de/db/conf/ht/ht2006.html#WuZM06}, year = 2006 } @phdthesis{augeri2008graph, abstract = {Graphs express relationships among objects, such as the radio connectivity among nodes in unmanned vehicle swarms. Some applications may rank a swarm's nodes by their relative importance, for example, using the PageRank algorithm applied in certain search engines to order query responses. The PageRank values of the nodes correspond to a unique eigenvector that can be computed using the power method, an iterative technique based on matrix multiplication. The first result is a practical lower bound on the PageRank algorithm's execution time that is derived by applying assumptions to the PageRank perturbation's scaling value and the PageRank vector's required numerical precision. The second result establishes nodes contained in the same block of the graph's coarsest equitable partition must have equal PageRank values. The third result, the AverageRank algorithm, ensures such nodes are assigned equal PageRank values. The fourth result, the ProductRank algorithm, reduces the time needed to find the PageRank vector by eliminating certain dot products in the power method if the graph's coarsest equitable partition contains blocks composed of multiple vertices. The fifth result, the QuotientRank algorithm, uses a quotient matrix induced by the coarsest equitable partition to further reduce the time needed to compute a swarm's PageRank vector.}, address = {Wright-Patterson Air Force Base, Ohio}, author = {Augeri, Christopher J.}, interhash = {ae4510331651ba7525daa04479a065ca}, intrahash = {af40ef13e09f4dda128456130bd491de}, month = {September}, school = {Air Force Institute of Technology}, title = {On Graph Isomorphism and the PageRank Algorithm}, url = {http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA490530}, year = 2008 } @inproceedings{hotho2006information, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.}, address = {Heidelberg}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {10ec64d80b0ac085328a953bb494fb89}, intrahash = {3c301945817681d637ee43901c016939}, month = {June}, pages = {411-426}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Information Retrieval in Folksonomies: Search and Ranking}, volume = 4011, year = 2006 }