{"id":12561,"date":"2022-04-05T13:23:37","date_gmt":"2022-04-05T17:23:37","guid":{"rendered":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=12561"},"modified":"2022-04-05T13:23:37","modified_gmt":"2022-04-05T17:23:37","slug":"how-aws-uses-graph-neural-networks-to-meet-customer-needs","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/how-aws-uses-graph-neural-networks-to-meet-customer-needs\/12561\/","title":{"rendered":"How AWS Uses Graph Neural Networks to Meet Customer Needs"},"content":{"rendered":"Originally published in Amazon Science, March 24, 2022. Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool. Graphs are an information-rich way to represent data. A graph consists of nodes \u2014 typically represented by circles \u2014 and edges \u2014 typically represented as line segments between nodes. In a knowledge graph, for instance, the nodes represent entities, and the edges represent relationships between them. In a social graph, the nodes represent people, and an edge indicates that two of those people know each other. At Amazon Web Services, the use of machine <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/how-aws-uses-graph-neural-networks-to-meet-customer-needs\/12561\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Originally published in Amazon Science, March 24, 2022. Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool. Graphs are an information-rich way to represent data. A graph consists of nodes \u2014 typically represented by circles \u2014 and edges \u2014 typically represented as line segments between nodes. In a [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[],"class_list":["post-12561","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/12561","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=12561"}],"version-history":[{"count":1,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/12561\/revisions"}],"predecessor-version":[{"id":12562,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/12561\/revisions\/12562"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=12561"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=12561"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=12561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}