{"id":1603,"date":"2023-03-13T20:23:37","date_gmt":"2023-03-13T20:23:37","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1603"},"modified":"2023-03-13T20:23:37","modified_gmt":"2023-03-13T20:23:37","slug":"vol19-no2-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no2\/vol19-no2-art5\/","title":{"rendered":"Uma Introdu\u00e7\u00e3o Amig\u00e1vel \u00e0s Redes Neurais para Grafos"},"content":{"rendered":"<p>Tiago da Silva <a href=\"https:\/\/orcid.org\/0009-0004-4581-9850\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Amauri Holanda de Souza J\u00fanior <a href=\"https:\/\/orcid.org\/0000-0002-2912-0781\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, &amp; Diego Parente Paiva Mesquita <a href=\"https:\/\/orcid.org\/0000-0002-9061-7041\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Graph neural networks have driven a series of recent developments in, e.g., drug discovery, recommender systems, and social network analysis. At their core, GNNs are designed to extract numerical representations for each node in a graph, recursively combining representations of neighboring nodes. This tutorial paper covers some popular and influential GNN models, and discusses their applications in different disciplines. We hope this work will help popularize GNNs in the local community, and foster scientific advances in machine learning and data science. <\/p>\n<p><strong>Keywords:<\/strong> Graph neural networks, geometric deep learning, graph machine learning.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol19-no2-art5\">10.21528\/lnlm-vol19-no2-art5<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/12\/vol19-no2-art5.pdf\">vol19-no2-art5.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/12\/vol19-no2-art5.bib\">vol19-no2-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tiago da Silva , Amauri Holanda de Souza J\u00fanior , &amp; Diego Parente Paiva Mesquita Abstract: Graph neural networks have driven a series of recent developments in, e.g., drug discovery, recommender systems, and social network analysis. At their core, GNNs <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no2\/vol19-no2-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1443,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1603","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Uma Introdu\u00e7\u00e3o Amig\u00e1vel \u00e0s Redes Neurais para Grafos - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no2\/vol19-no2-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uma Introdu\u00e7\u00e3o Amig\u00e1vel \u00e0s Redes Neurais para Grafos - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Tiago da Silva , Amauri Holanda de Souza J\u00fanior , &amp; Diego Parente Paiva Mesquita Abstract: Graph neural networks have driven a series of recent developments in, e.g., drug discovery, recommender systems, and social network analysis. 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