PUMA publications for /tag/pagerankhttps://puma.uni-kassel.de/tag/pagerankPUMA RSS feed for /tag/pagerank2024-03-29T14:52:44+01:00Topical PageRank: A Model of Scientific Expertise for Bibliographic Searchhttps://puma.uni-kassel.de/bibtex/2f4620195b04beda98c3f7336c4b96dd5/stephandoerfelstephandoerfel2014-09-26T14:16:29+02:00bibliographic-search eacl2014 james jardine model pagerank searching topical <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="James Jardine" itemprop="url" href="/author/James%20Jardine"><span itemprop="name">J. Jardine</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Simone Teufel" itemprop="url" href="/author/Simone%20Teufel"><span itemprop="name">S. Teufel</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics</span>, </em></span><em>Seite <span itemprop="pagination">501--510</span>. </em><em>Gothenburg, Sweden, </em><em><span itemprop="publisher">Association for Computational Linguistics</span>, </em>(<em><span>April 2014<meta content="April 2014" itemprop="datePublished"/></span></em>)Fri Sep 26 14:16:29 CEST 2014Gothenburg, SwedenProceedings of the 14th Conference of the European Chapter of the Association for Computational LinguisticsApril501--510Topical PageRank: A Model of Scientific Expertise for Bibliographic Search2014bibliographic-search eacl2014 james jardine model pagerank searching topical Full-Text Citation Analysis: A New Method to Enhance Scholarly Networkhttps://puma.uni-kassel.de/bibtex/2f9c6133bf4503003822f99860f864698/hothohotho2013-04-24T15:01:15+02:00analysis citation classification graph pagerank ranking scientometrics sota <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiaozhong Liu" itemprop="url" href="/author/Xiaozhong%20Liu"><span itemprop="name">X. Liu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jinsong Zhang" itemprop="url" href="/author/Jinsong%20Zhang"><span itemprop="name">J. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chun Guo" itemprop="url" href="/author/Chun%20Guo"><span itemprop="name">C. Guo</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of the American Society for Information Science and Technology</em></span></span> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Wed Apr 24 15:01:15 CEST 2013Journal of the American Society for Information Science and TechnologyFull-Text Citation Analysis: A New Method to Enhance Scholarly Network2012analysis citation classification graph pagerank ranking scientometrics sota Full-Text Citation Analysis: A New Method to Enhance Scholarly Networkhttps://puma.uni-kassel.de/bibtex/2f9c6133bf4503003822f99860f864698/jaeschkejaeschke2013-04-24T15:00:45+02:00analysis citation classification graph pagerank ranking scientometrics sota topic <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiaozhong Liu" itemprop="url" href="/author/Xiaozhong%20Liu"><span itemprop="name">X. Liu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jinsong Zhang" itemprop="url" href="/author/Jinsong%20Zhang"><span itemprop="name">J. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chun Guo" itemprop="url" href="/author/Chun%20Guo"><span itemprop="name">C. Guo</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of the American Society for Information Science and Technology</em></span></span> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Wed Apr 24 15:00:45 CEST 2013Journal of the American Society for Information Science and TechnologyFull-Text Citation Analysis: A New Method to Enhance Scholarly Network2012analysis citation classification graph pagerank ranking scientometrics sota topic Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structurehttps://puma.uni-kassel.de/bibtex/2e4769d86e71c9e7ba77d5d4af6f21e0c/jaeschkejaeschke2013-02-21T14:49:24+01:00analysis history link pagerank wikipedia <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yuku Takahashi" itemprop="url" href="/author/Yuku%20Takahashi"><span itemprop="name">Y. Takahashi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hiroaki Ohshima" itemprop="url" href="/author/Hiroaki%20Ohshima"><span itemprop="name">H. Ohshima</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mitsuo Yamamoto" itemprop="url" href="/author/Mitsuo%20Yamamoto"><span itemprop="name">M. Yamamoto</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hirotoshi Iwasaki" itemprop="url" href="/author/Hirotoshi%20Iwasaki"><span itemprop="name">H. Iwasaki</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Satoshi Oyama" itemprop="url" href="/author/Satoshi%20Oyama"><span itemprop="name">S. Oyama</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Katsumi Tanaka" itemprop="url" href="/author/Katsumi%20Tanaka"><span itemprop="name">K. Tanaka</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 22nd ACM conference on Hypertext and hypermedia</span>, </em></span><em>Seite <span itemprop="pagination">83--92</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Thu Feb 21 14:49:24 CET 2013New York, NY, USAProceedings of the 22nd ACM conference on Hypertext and hypermedia83--92Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structure2011analysis history link pagerank wikipedia We propose a method to evaluate the significance of historical entities (people, events, and so on.). Here, the significance of a historical entity means how it affected other historical entities. Our proposed method first calculates the tempo-spacial impact of historical entities. The impact of a historical entity varies according to time and location. Historical entities are collected from Wikipedia. We assume that a Wikipedia link between historical entities represents an impact propagation. That is, when an entity has a link to another entity, we regard the former is influenced by the latter. Historical entities in Wikipedia usually have the date and location of their occurrence. Our proposed iteration algorithm propagates such initial tempo-spacial information through links in the similar manner as PageRank, so the tempo-spacial impact scores of all the historical entities can be calculated. We assume that a historical entity is significant if it influences many other entities that are far from it temporally or geographically. We demonstrate a prototype system and show the results of experiments that prove the effectiveness of our method.Finding related pages using Green measures: an illustration with Wikipediahttps://puma.uni-kassel.de/bibtex/276e219fe6e8a257b30c6665af8b273da/jaeschkejaeschke2013-02-21T14:25:41+01:00analysis link pagerank similarity wikipedia <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yann Ollivier" itemprop="url" href="/author/Yann%20Ollivier"><span itemprop="name">Y. Ollivier</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pierre Senellart" itemprop="url" href="/author/Pierre%20Senellart"><span itemprop="name">P. Senellart</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 22nd national conference on Artificial intelligence</span>, </em></span><em> 2, </em><em>Seite <span itemprop="pagination">1427--1433</span>. </em><em><span itemprop="publisher">AAAI Press</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Feb 21 14:25:41 CET 2013Proceedings of the 22nd national conference on Artificial intelligence1427--1433Finding related pages using Green measures: an illustration with Wikipedia22007analysis link pagerank similarity wikipedia We introduce a new method for finding nodes semantically related to a given node in a hyperlinked graph: the Green method, based on a classical Markov chain tool. It is generic, adjustment-free and easy to implement. We test it in the case of the hyperlink structure of the English version of Wikipedia, the on-line encyclopedia. We present an extensive comparative study of the performance of our method versus several other classical methods in the case of Wikipedia. The Green method is found to have both the best average results and the best robustness.On the temporal dimension of searchhttps://puma.uni-kassel.de/bibtex/216f2087be646ae8430fd0ff514ec0cf1/stephandoerfelstephandoerfel2013-01-02T17:43:01+01:00info20 pagerank temporal <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philip S. Yu" itemprop="url" href="/author/Philip%20S.%20Yu"><span itemprop="name">P. Yu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xin Li" itemprop="url" href="/author/Xin%20Li"><span itemprop="name">X. Li</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bing Liu" itemprop="url" href="/author/Bing%20Liu"><span itemprop="name">B. Liu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters</span>, </em></span><em>Seite <span itemprop="pagination">448--449</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Wed Jan 02 17:43:01 CET 2013New York, NY, USAProceedings of the 13th international World Wide Web conference on Alternate track papers \& posters448--449WWW Alt. '04On the temporal dimension of search2004info20 pagerank temporal Web search is probably the single most important application on the Internet. The most famous search techniques are perhaps the PageRank and HITS algorithms. These algorithms are motivated by the observation that a hyperlink from a page to another is an implicit conveyance of authority to the target page. They exploit this social phenomenon to identify quality pages, e.g., "authority" pages and "hub" pages. In this paper we argue that these algorithms miss an important dimension of the Web, the temporal dimension. The Web is not a static environment. It changes constantly. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Bringing new and quality pages to users is important because most users want the latest information. Research publication search has exactly the same problem. This paper studies the temporal dimension of search in the context of research publication search. We propose a number of methods deal with the problem. Our experimental results show that these methods are highly effective.On the temporal dimension of searchPageRank on an evolving graphhttps://puma.uni-kassel.de/bibtex/26058356e9c5a62b3993686ff5eac9529/jaeschkejaeschke2012-09-06T10:49:09+02:00engine pagerank search time web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bahman Bahmani" itemprop="url" href="/author/Bahman%20Bahmani"><span itemprop="name">B. Bahmani</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ravi Kumar" itemprop="url" href="/author/Ravi%20Kumar"><span itemprop="name">R. Kumar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mohammad Mahdian" itemprop="url" href="/author/Mohammad%20Mahdian"><span itemprop="name">M. Mahdian</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Eli Upfal" itemprop="url" href="/author/Eli%20Upfal"><span itemprop="name">E. Upfal</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining</span>, </em></span><em>Seite <span itemprop="pagination">24--32</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Thu Sep 06 10:49:09 CEST 2012New York, NY, USAProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining24--32PageRank on an evolving graph2012engine pagerank search time web One of the most important features of the Web graph and social networks is that they are constantly evolving. The classical computational paradigm, which assumes a fixed data set as an input to an algorithm that terminates, is inadequate for such settings. In this paper we study the problem of computing PageRank on an evolving graph. We propose an algorithm that, at any moment in the time and by crawling a small portion of the graph, provides an estimate of the PageRank that is close to the true PageRank of the graph at that moment. We will also evaluate our algorithm experimentally on real data sets and on randomly generated inputs. Under a stylized model of graph evolution, we show that our algorithm achieves a provable performance guarantee that is significantly better than the naive algorithm that crawls the nodes in a round-robin fashion.RankMass crawler: a crawler with high personalized pagerank coverage guaranteehttps://puma.uni-kassel.de/bibtex/23227ef077a463fbaa6ba1ac7aac82d06/jaeschkejaeschke2012-09-06T09:55:36+02:00crawling engine pagerank search web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Junghoo Cho" itemprop="url" href="/author/Junghoo%20Cho"><span itemprop="name">J. Cho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Uri Schonfeld" itemprop="url" href="/author/Uri%20Schonfeld"><span itemprop="name">U. Schonfeld</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 33rd international conference on Very large data bases</span>, </em></span><em>Seite <span itemprop="pagination">375--386</span>. </em><em><span itemprop="publisher">VLDB Endowment</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Sep 06 09:55:36 CEST 2012Proceedings of the 33rd international conference on Very large data bases375--386RankMass crawler: a crawler with high personalized pagerank coverage guarantee2007crawling engine pagerank search web Crawling algorithms have been the subject of extensive research and optimizations, but some important questions remain open. In particular, given the unbounded number of pages available on the Web, search-engine operators constantly struggle with the following vexing questions: <i>When can I stop downloading the Web? How many pages should I download to cover "most" of the Web? How can I know I am not missing an important part when I stop?</i> In this paper we provide an answer to these questions by developing, in the context of a system that is given a set of trusted pages, a family of crawling algorithms that (1) provide a theoretical guarantee on how much of the "important" part of the Web it will download after crawling a certain number of pages and (2) give a high priority to important pages during a crawl, so that the search engine can index the most important part of the Web first. We prove the correctness of our algorithms by theoretical analysis and evaluate their performance experimentally based on 141 million URLs obtained from the Web. Our experiments demonstrate that even our simple algorithm is effective in downloading important pages early on and provides high "coverage" of the Web with a relatively small number of pages.Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Searchhttps://puma.uni-kassel.de/bibtex/234aedd24fc7a45f189be1ca70dfd99e2/hothohotho2012-06-10T17:41:58+02:00pagerank ranking search topic web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Taher H. Haveliwala" itemprop="url" href="/author/Taher%20H.%20Haveliwala"><span itemprop="name">T. Haveliwala</span></a></span>. </span><em><span itemprop="educationalUse">Technical Report</span>, </em><em>2003-29. </em><em><span itemprop="producer">Stanford InfoLab</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Sun Jun 10 17:41:58 CEST 2012IEEE Transactions on Knowledge and Data EngineeringExtended version of the WWW2002 paper on Topic-Sensitive PageRank.2003-29Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web SearchTechnical Report2003pagerank ranking search topic web The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative ``importance'' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a {\em set} of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developershttps://puma.uni-kassel.de/bibtex/28ad9a4592710e41d9d7fb9eba0cee79c/stummestumme2012-01-04T11:19:46+01:002011 CVS Contact Development Mining PageRank Proximity contact development itegpub mining pagerank <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title=" Björn-Elmar Macek, Martin Atzmueller, Gerd Stumme" itemprop="url" href="/author/null%20%7bBj%c3%b6rn-Elmar%20Macek,%20Martin%20Atzmueller,%20Gerd%20Stumme%7d"><span itemprop="name">Björn-Elmar Macek, Martin Atzmueller, Gerd Stumme</span></a></span>. </span><em> Proc. IEEE SocialCom, </em><em>Boston, MA, USA, </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Jan 04 11:19:46 CET 2012Boston, MA, USAProfile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software DevelopersProc. IEEE SocialCom20112011 CVS Contact Development Mining PageRank Proximity contact development itegpub mining pagerank Personalized PageRank vectors for tag recommendations: inside FolkRankhttps://puma.uni-kassel.de/bibtex/2f022e60c5928e01c701d7ec539ec221b/jaeschkejaeschke2011-12-21T22:52:09+01:00bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heung-Nam Kim" itemprop="url" href="/author/Heung-Nam%20Kim"><span itemprop="name">H. Kim</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abdulmotaleb El Saddik" itemprop="url" href="/author/Abdulmotaleb%20El%20Saddik"><span itemprop="name">A. El Saddik</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the fifth ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">45--52</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Dec 21 22:52:09 CET 2011New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52Personalized PageRank vectors for tag recommendations: inside FolkRank2011bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.The anatomy of a large-scale hypertextual Web search enginehttps://puma.uni-kassel.de/bibtex/21779c82bd34bbf1ca62956d136a22adf/stephandoerfelstephandoerfel2011-12-20T19:40:59+01:00google pagerank <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sergey Brin" itemprop="url" href="/author/Sergey%20Brin"><span itemprop="name">S. Brin</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lawrence Page" itemprop="url" href="/author/Lawrence%20Page"><span itemprop="name">L. Page</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Computer Networks and ISDN Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">1--7</span>):
<span itemprop="pagination">107--117</span></em> </span>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Tue Dec 20 19:40:59 CET 2011Computer Networks and ISDN Systems1--7107--117The anatomy of a large-scale hypertextual Web search engine301998google pagerank In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://infolab.stanford.edu/~backrub/google.html To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description w...Centrality Indiceshttps://puma.uni-kassel.de/bibtex/2567d2f61b08e78af53463b2a30729830/stephandoerfelstephandoerfel2011-12-19T14:07:38+01:00analysis brandes centrality index network pagerank sna social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dirk Koschützki" itemprop="url" href="/author/Dirk%20Kosch%c3%bctzki"><span itemprop="name">D. Koschützki</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Katharina Lehmann" itemprop="url" href="/author/Katharina%20Lehmann"><span itemprop="name">K. Lehmann</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leon Peeters" itemprop="url" href="/author/Leon%20Peeters"><span itemprop="name">L. Peeters</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Richter" itemprop="url" href="/author/Stefan%20Richter"><span itemprop="name">S. Richter</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dagmar Tenfelde-Podehl" itemprop="url" href="/author/Dagmar%20Tenfelde-Podehl"><span itemprop="name">D. Tenfelde-Podehl</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Oliver Zlotowski" itemprop="url" href="/author/Oliver%20Zlotowski"><span itemprop="name">O. Zlotowski</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Network Analysis</span>, </em><em>Volume 3418 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin / Heidelberg, </em></span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Mon Dec 19 14:07:38 CET 2011Berlin / HeidelbergNetwork Analysis16-61Lecture Notes in Computer ScienceCentrality Indices34182005analysis brandes centrality index network pagerank sna social Centrality indices are to quantify an intuitive feeling that in most networks some vertices or edges are more central than others. Many vertex centrality indices were introduced for the first time in the 1950s: e.g., the Bavelas index [50, 51], degree centrality [483] or a first feedback centrality, introduced by Seeley [510]. These early centralities raised a rush of research in which manifold applications were found. However, not every centrality index was suitable to every application, so with time, dozens of new centrality indices were published. This chapter will present some of the more influential, ‘classic’ centrality indices. We do not strive for completeness, but hope to give a catalog of basic centrality indices with some of their main applications.Export Citation - SpringerLinkBrowseRank: letting web users vote for page importancehttps://puma.uni-kassel.de/bibtex/22eeaa8699f0c0e86c47fbbbf445f6022/jaeschkejaeschke2011-12-06T19:07:29+01:00browserank search social web pagerank ranking <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yuting Liu" itemprop="url" href="/author/Yuting%20Liu"><span itemprop="name">Y. Liu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bin Gao" itemprop="url" href="/author/Bin%20Gao"><span itemprop="name">B. Gao</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tie-Yan Liu" itemprop="url" href="/author/Tie-Yan%20Liu"><span itemprop="name">T. Liu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ying Zhang" itemprop="url" href="/author/Ying%20Zhang"><span itemprop="name">Y. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zhiming Ma" itemprop="url" href="/author/Zhiming%20Ma"><span itemprop="name">Z. Ma</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shuyuan He" itemprop="url" href="/author/Shuyuan%20He"><span itemprop="name">S. He</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hang Li" itemprop="url" href="/author/Hang%20Li"><span itemprop="name">H. Li</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval</span>, </em></span><em>Seite <span itemprop="pagination">451--458</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Tue Dec 06 19:07:29 CET 2011New York, NY, USAProceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval451--458BrowseRank: letting web users vote for page importance2008browserank search social web pagerank ranking This paper proposes a new method for computing page importance, referred to as BrowseRank. The conventional approach to compute page importance is to exploit the link graph of the web and to build a model based on that graph. For instance, PageRank is such an algorithm, which employs a discrete-time Markov process as the model. Unfortunately, the link graph might be incomplete and inaccurate with respect to data for determining page importance, because links can be easily added and deleted by web content creators. In this paper, we propose computing page importance by using a 'user browsing graph' created from user behavior data. In this graph, vertices represent pages and directed edges represent transitions between pages in the users' web browsing history. Furthermore, the lengths of staying time spent on the pages by users are also included. The user browsing graph is more reliable than the link graph for inferring page importance. This paper further proposes using the <i>continuous-time</i> Markov process on the user browsing graph as a model and computing the stationary probability distribution of the process as page importance. An efficient algorithm for this computation has also been devised. In this way, we can leverage hundreds of millions of users' implicit voting on page importance. Experimental results show that BrowseRank indeed outperforms the baseline methods such as PageRank and TrustRank in several tasks.PeopleRank: Social Opportunistic Forwardinghttps://puma.uni-kassel.de/bibtex/2ecc71dabeaae2b46aef3afee17e3dcdc/jaeschkejaeschke2011-12-06T18:38:00+01:00peoplerank search social web pagerank ranking <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abderrahmen Mtibaa" itemprop="url" href="/author/Abderrahmen%20Mtibaa"><span itemprop="name">A. Mtibaa</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin May" itemprop="url" href="/author/Martin%20May"><span itemprop="name">M. May</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christophe Diot" itemprop="url" href="/author/Christophe%20Diot"><span itemprop="name">C. Diot</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mostafa H. Ammar" itemprop="url" href="/author/Mostafa%20H.%20Ammar"><span itemprop="name">M. Ammar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">INFOCOM, 2010 Proceedings IEEE</span>, </em></span><em>Seite <span itemprop="pagination">1--5</span>. </em><em><span itemprop="publisher">IEEE</span>, </em>(<em><span>März 2010<meta content="März 2010" itemprop="datePublished"/></span></em>)Tue Dec 06 18:38:00 CET 2011INFOCOM, 2010 Proceedings IEEEmarch1--5PeopleRank: Social Opportunistic Forwarding2010peoplerank search social web pagerank ranking In opportunistic networks, end-to-end paths between two communicating nodes are rarely available. In such situations, the nodes might still copy and forward messages to nodes that are more likely to meet the destination. The question is which forwarding algorithm offers the best trade off between cost (number of message replicas) and rate of successful message delivery. We address this challenge by developing the PeopleRank approach in which nodes are ranked using a tunable weighted social information. Similar to the PageRank idea, PeopleRank gives higher weight to nodes if they are socially connected to important other nodes of the network. We develop centralized and distributed variants for the computation of PeopleRank. We present an evaluation using real mobility traces of nodes and their social interactions to show that PeopleRank manages to deliver messages with near optimal success rate (close to Epidemic Routing) while reducing the number of message retransmissions by 50% compared to Epidemic Routing.An Analytical Comparison of Approaches to Personalizing PageRankhttps://puma.uni-kassel.de/bibtex/2c0a97c488805a3b4349339439376ac44/jaeschkejaeschke2011-12-06T18:28:02+01:00comparison personalization search social web pagerank ranking <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Taher Haveliwala" itemprop="url" href="/author/Taher%20Haveliwala"><span itemprop="name">T. Haveliwala</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sepandar Kamvar" itemprop="url" href="/author/Sepandar%20Kamvar"><span itemprop="name">S. Kamvar</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Glen Jeh" itemprop="url" href="/author/Glen%20Jeh"><span itemprop="name">G. Jeh</span></a></span>. </span><em>2003-35. </em><em><span itemprop="producer">Stanford InfoLab</span>, </em><em>Stanford, </em>(<em><span>Juni 2003<meta content="Juni 2003" itemprop="datePublished"/></span></em>)Tue Dec 06 18:28:02 CET 2011Stanfordjun2003-35An Analytical Comparison of Approaches to Personalizing PageRank2003comparison personalization search social web pagerank ranking PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of "importance" to web pages. Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way. We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one.FolkRank: A Ranking Algorithm for Folksonomieshttps://puma.uni-kassel.de/bibtex/24d8b4f79814691fbe6db8357d63206a1/itegiteg2011-11-22T10:26:32+01:002006 algorithm folkrank ir itegpub l3s myown nepomuk pagerank ranking <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. FGIR 2006</span>, </em></span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Proc. FGIR 2006FolkRank: A Ranking Algorithm for Folksonomies20062006 algorithm folkrank ir itegpub l3s myown nepomuk pagerank ranking In social bookmark tools users are setting up
lightweight conceptual structures called folksonomies. Currently,
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. A long version of this paper
has been published at the European Semantic Web Conference
2006.Information Retrieval in Folksonomies: Search and Rankinghttps://puma.uni-kassel.de/bibtex/2b1e4dabc5b558aeea1b839a7f123eef1/itegiteg2011-11-22T10:26:32+01:002006 FCA IR OntologyHandbook folkrank folksonomy information informationretrieval itegpub mimose myown pagerank ranking retrieval <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert J?schke" itemprop="url" href="/author/Robert%20J%3fschke"><span itemprop="name">R. J?schke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von LNAI, </em><em>Seite <span itemprop="pagination">411-426</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Juni 2006<meta content="Juni 2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and ApplicationsJune411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking401120062006 FCA IR OntologyHandbook folkrank folksonomy information informationretrieval itegpub mimose myown pagerank ranking retrieval Information Retrieval in Folksonomies: Search and Rankinghttps://puma.uni-kassel.de/bibtex/23c301945817681d637ee43901c016939/itegiteg2011-11-22T10:26:32+01:002006 FCA IR OntologyHandbook folkrank folksonomy information informationretrieval itegpub mimose myown pagerank ranking retrieval <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von LNAI, </em><em>Seite <span itemprop="pagination">411-426</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Juni 2006<meta content="Juni 2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and ApplicationsJune411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking401120062006 FCA IR OntologyHandbook folkrank folksonomy information informationretrieval itegpub mimose myown pagerank ranking retrieval