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dc.contributor.authorThelwall, Mike
dc.contributor.authorWilkinson, David
dc.date.accessioned2008-05-22T08:32:09Z
dc.date.available2008-05-22T08:32:09Z
dc.date.issued2003
dc.identifier.citationJournal of the American Society for Information Science and Technology, 54 (8): 706-712
dc.identifier.issn15322882
dc.identifier.issn15322890
dc.identifier.doi10.1002/asi.10267
dc.identifier.urihttp://hdl.handle.net/2436/27518
dc.description.abstractThe graph structures of three national university publicly indexable Webs from Australia, New Zealand, and the UK were analyzed. Strong scale-free regularities for page indegrees, outdegrees, and connected component sizes were in evidence, resulting in power laws similar to those previously identified for individual university Web sites and for the AltaVista-indexed Web. Anomalies were also discovered in most distributions and were tracked down to root causes. As a result, resource driven Web sites and automatically generated pages were identified as representing a significant break from the assumptions of previous power law models. It follows that attempts to track average Web linking behavior would benefit from using techniques to minimize or eliminate the impact of such anomalies.
dc.language.isoen
dc.publisherWiley
dc.relation.urlhttp://www3.interscience.wiley.com/journal/104525197/abstract
dc.subjectColleges
dc.subjectUniversities
dc.subjectGraphs
dc.subjectWebometrics
dc.subjectHypertext
dc.subjectMaps (graphic representation)
dc.subjectLink analysis
dc.subjectWebsites
dc.subjectAcademic websites
dc.subjectLinks (hypermedia)
dc.titleGraph structure in three national academic Webs: Power laws with anomalies
dc.typeJournal article
dc.identifier.journalJournal of the American Society for Information Science and Technology
html.description.abstractThe graph structures of three national university publicly indexable Webs from Australia, New Zealand, and the UK were analyzed. Strong scale-free regularities for page indegrees, outdegrees, and connected component sizes were in evidence, resulting in power laws similar to those previously identified for individual university Web sites and for the AltaVista-indexed Web. Anomalies were also discovered in most distributions and were tracked down to root causes. As a result, resource driven Web sites and automatically generated pages were identified as representing a significant break from the assumptions of previous power law models. It follows that attempts to track average Web linking behavior would benefit from using techniques to minimize or eliminate the impact of such anomalies.


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