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  <title><![CDATA[Inference for unlabelled graphs]]></title>
  <body><![CDATA[<p><strong>TITLE: </strong>Inference for unlabelled
graphs</p><p><strong>SPEAKER: </strong>Professor Peter Bickel</p><p><strong>ABSTRACT:</strong></p><p>A great deal of attention has recently
been paid to determining sub-communities on the basis of relations,
corresponding to edges, between individuals, corresponding to
vertices&nbsp;&nbsp; of an unlabelled graph (Newman, SIAM Review 2003;
Airoldi et al JMLR 2008; Leskovec &amp;&nbsp;
Kleinberg et al SIGKDD 2005). We have developed a nonparametric
framework for probabilistic ergodic models of infinite unlabelled graphs (PNAS2009)and
made some connections with modularities arising in the physics literature &nbsp;&nbsp;and community models in the social sciences.A
fundamental difficulty in implementing these procedures is&nbsp; computational complexity.We &nbsp;develop&nbsp;&nbsp;
approaches&nbsp; which &nbsp;bypass these difficulties.</p>

<p>&nbsp;(This is joint work with Aiyou Chen and Liza
Levina)</p>]]></body>
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      <value><![CDATA[2010-09-14T13:00:00-04:00]]></value>
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