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	<title>graph Archives - Petamind</title>
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		<title>Create bipartite graph from a rating matrix</title>
		<link>https://petaminds.com/create-bipartite-graph-from-a-rating-matrix/</link>
					<comments>https://petaminds.com/create-bipartite-graph-from-a-rating-matrix/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 15 Mar 2020 01:41:19 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[bipartite]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[graph]]></category>
		<category><![CDATA[movie lens]]></category>
		<category><![CDATA[networkx]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2305</guid>

					<description><![CDATA[<p>As deep learning on graphs is trending recently, this article will quickly demonstrate how to use networkx to turn rating matrices, such as MovieLens dataset, into graph data. The rating data We use rating data from the movie lens. The rating data is loaded into rdata which is a Pandas DataFrame. This article demonstrates how [&#8230;]</p>
<p>The post <a href="https://petaminds.com/create-bipartite-graph-from-a-rating-matrix/">Create bipartite graph from a rating matrix</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>An example to Graph Convolutional Network</title>
		<link>https://petaminds.com/an-example-to-graph-convolutional-network/</link>
					<comments>https://petaminds.com/an-example-to-graph-convolutional-network/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 23 Sep 2019 02:03:09 +0000</pubDate>
				<category><![CDATA[back-end]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[gcn]]></category>
		<category><![CDATA[graph]]></category>
		<category><![CDATA[karate]]></category>
		<category><![CDATA[network]]></category>
		<category><![CDATA[node embedding]]></category>
		<guid isPermaLink="false">http://petaminds.com/?p=633</guid>

					<description><![CDATA[<p>In my research, there are many problems involve networks of different types, e.g. social network, online-trading networks, crowd-sourcing, etc. I was so happy to find a new powerful tool for my research, the graph convolutional network, which applies deep learning on graph structures. Graph convolutional network (GCN) There is currently no official definition for GCN. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/an-example-to-graph-convolutional-network/">An example to Graph Convolutional Network</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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