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The spectrum of a graph is often claimed to contain all the information within a graph, but the raw spectrum contains too much information to be directly used as a useful metric. In this paper we introduce a metric, the weighted spectral distribution, that improves on the raw spectrum by discounting those eigenvalues believed to be unimportant and emphasizing the contribution of those believed to be important.<\/jats:p>\n          <jats:p>We use this metric to optimize the selection of parameter values for generating Internet topologies. Our metric leads to parameter choices that appear sensible given prior knowledge of the problem domain: the resulting choices are close to the default values of the topology generators and, in the case of some generators, fall within the expected region. This metric provides a means for meaningfully optimizing parameter selection when generating topologies intended to share structure with, but not match exactly, measured graphs.<\/jats:p>","DOI":"10.1145\/1710115.1710129","type":"journal-article","created":{"date-parts":[[2010,1,26]],"date-time":"2010-01-26T14:01:38Z","timestamp":1264514498000},"page":"67-72","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A weighted spectrum metric for comparison of internet topologies"],"prefix":"10.1145","volume":"37","author":[{"given":"Damien","family":"Fay","sequence":"first","affiliation":[{"name":"National University of Ireland, Galway, Ireland"}]},{"given":"Hamed","family":"Haddadi","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Software Systems"}]},{"given":"Andrew W.","family":"Moore","sequence":"additional","affiliation":[{"name":"University of Cambridge"}]},{"given":"Richard","family":"Mortier","sequence":"additional","affiliation":[{"name":"Vipadia Ltd, Cambridge, United Kingdom"}]},{"given":"Steve","family":"Uhlig","sequence":"additional","affiliation":[{"name":"TU Berlin\/Deutsche Telekom Labs, Germany"}]},{"given":"Almerima","family":"Jamakovic","sequence":"additional","affiliation":[{"name":"TNO Netherlands, Rotterdam, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2010,1,21]]},"reference":[{"unstructured":"CAIDA skitter Internet topology measurement tool.  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Agreement beyong polarization: Spectral network analysis of congressional roll call votes . In Annual Meeting of the American Political Science Association , 2006 . M. Harding. Agreement beyong polarization: Spectral network analysis of congressional roll call votes. In Annual Meeting of the American Political Science Association, 2006."},{"volume-title":"A practical guide to support vector classification. Department of Computer Science","author":"Hsu C.-W.","unstructured":"C.-W. Hsu , C.-C. Chang , and C.-J. Lin . A practical guide to support vector classification. Department of Computer Science , National Taiwan University , Taipei 106, Taiwan. http:\/\/www.csie.ntu.edu.tw\/~cjlin\/papers\/guide\/guide.pdf. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classification. 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