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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Microsoft Research</author_name><author_url>https://www.microsoft.com/en-us/research</author_url><title>Topic-Partitioned Multinetwork Embeddings - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="pK8C0plJdl"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/topic-partitioned-multinetwork-embeddings/"&gt;Topic-Partitioned Multinetwork Embeddings&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/topic-partitioned-multinetwork-embeddings/embed/#?secret=pK8C0plJdl" width="600" height="338" title="&#x201C;Topic-Partitioned Multinetwork Embeddings&#x201D; &#x2014; Microsoft Research" data-secret="pK8C0plJdl" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>We introduce a new Bayesian admixture model intended for exploratory analysis of communication networks&#x2014;specifically, the discovery and visualization of topic-specific subnetworks in email data sets. Our model produces principled visualizations of email networks, i.e., visualizations that have precise mathematical interpretations in terms of our model and its relationship to the observed data. We validate our [&hellip;]</description></oembed>
