Finding Communities in Typed Citation Networks
dc.contributor.author | Kroon, Frederick William | |
dc.date.accessioned | 2008-09-25T15:50:55Z | |
dc.date.available | 2008-09-25T15:50:55Z | |
dc.date.issued | 2008-09-25T15:50:55Z | |
dc.date.submitted | 2008 | |
dc.description.abstract | As the Web has become more and more important to our daily lives, algorithms that can effectively utilize the link structure have become more and more important. One such task has been to find communities in social network data. Recently, however, there has been increased interest in augmenting links with additional semantic information. We examine link classification from the point of view of scientometrics, with an eye towards applying what has been learned about scientific citation to Web linking. Some community detection algorithms are reviewed, and one that has been developed for topical community finding on the Web is adapted to typed scientific citations. | en |
dc.identifier.uri | http://hdl.handle.net/10012/4037 | |
dc.language.iso | en | en |
dc.pending | false | en |
dc.publisher | University of Waterloo | en |
dc.subject | citation classification | en |
dc.subject | scientometrics | en |
dc.subject | social networks | en |
dc.subject | community detection | en |
dc.subject | bibliometrics | en |
dc.subject | information retrieval | en |
dc.subject.program | Computer Science | en |
dc.title | Finding Communities in Typed Citation Networks | en |
dc.type | Master Thesis | en |
uws-etd.degree | Master of Mathematics | en |
uws-etd.degree.department | School of Computer Science | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |