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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>Milan Vojnovic</author_name><author_url>https://www.microsoft.com/en-us/research/people/milanv/</author_url><title>Balanced Graph Edge Partition - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="Be80146qLv"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/balanced-graph-edge-partition/"&gt;Balanced Graph Edge Partition&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/balanced-graph-edge-partition/embed/#?secret=Be80146qLv" width="600" height="338" title="&#x201C;Balanced Graph Edge Partition&#x201D; &#x2014; Microsoft Research" data-secret="Be80146qLv" 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>Balanced edge partition has emerged as a new approach to partition an input graph data for the purpose of scaling out parallel computations, which is of interest for several modern data analytics computation platforms, including platforms for iterative computations, machine learning problems, and graph databases. This new approach stands in a stark contrast to the [&hellip;]</description></oembed>
