{"id":477375,"date":"2023-08-09T09:11:34","date_gmt":"2023-08-09T09:11:34","guid":{"rendered":""},"modified":"2023-09-05T11:14:34","modified_gmt":"2023-09-05T11:14:34","slug":"graph-neural-networks","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/graph-neural-networks\/","title":{"rendered":"Grafik sinir a\u011flar\u0131"},"content":{"rendered":"<h2>Grafik Sinir A\u011flar\u0131na Genel Bak\u0131\u015f<\/h2>\n<p>Grafik Sinir A\u011flar\u0131 (GNN&#039;ler), grafik yap\u0131l\u0131 verileri yakalamay\u0131 ve y\u00f6netmeyi ama\u00e7layan, makine \u00f6\u011frenimi ve yapay zeka alan\u0131nda \u00f6nemli bir geli\u015fmeyi temsil eder. Temel olarak GNN&#039;ler, grafik olarak yap\u0131land\u0131r\u0131lm\u0131\u015f veriler \u00fczerinde \u00e7al\u0131\u015fmak \u00fczere \u00f6zel olarak tasarlanm\u0131\u015f ve geleneksel sinir a\u011flar\u0131n\u0131n u\u011fra\u015ft\u0131\u011f\u0131 \u00e7ok \u00e7e\u015fitli sorunlar\u0131n \u00fcstesinden gelmelerine olanak tan\u0131yan bir t\u00fcr sinir a\u011f\u0131d\u0131r. Bu, sosyal a\u011f temsilini, \u00f6neri sistemlerini, biyolojik verilerin yorumlanmas\u0131n\u0131 ve a\u011f trafi\u011fi analizini i\u00e7erir ancak bunlarla s\u0131n\u0131rl\u0131 de\u011fildir.<\/p>\n<h2>Grafik Sinir A\u011flar\u0131n\u0131n Tarih\u00e7esi ve Ortaya \u00c7\u0131k\u0131\u015f\u0131<\/h2>\n<p>GNN kavram\u0131 ilk olarak 2000&#039;li y\u0131llar\u0131n ba\u015f\u0131nda Franco Scarselli, Marco Gori ve di\u011ferlerinin \u00e7al\u0131\u015fmalar\u0131yla ortaya \u00e7\u0131kt\u0131. Bir d\u00fc\u011f\u00fcm\u00fcn yerel kom\u015fulu\u011funu yinelemeli bir tarzda analiz edecek orijinal Grafik Sinir A\u011f\u0131 modelini geli\u015ftirdiler. Ancak bu orijinal model, hesaplama verimlili\u011fi ve \u00f6l\u00e7eklenebilirlik konusunda zorluklarla kar\u015f\u0131la\u015ft\u0131.<\/p>\n<p>Genellikle Grafik Evri\u015fimli A\u011flar (GCN&#039;ler) olarak an\u0131lan Evri\u015fimli Sinir A\u011flar\u0131n\u0131n (CNN&#039;ler) grafiklerde kullan\u0131ma sunulmas\u0131na kadar GNN&#039;ler daha fazla ilgi kazanmaya ba\u015flad\u0131. Thomas N. Kipf ve Max Welling&#039;in 2016&#039;daki \u00e7al\u0131\u015fmas\u0131 bu kavram\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde pop\u00fcler hale getirerek GNN alan\u0131na sa\u011flam bir temel kazand\u0131rd\u0131.<\/p>\n<h2>Konuyu Geni\u015fletmek: Grafik Sinir A\u011flar\u0131<\/h2>\n<p>Grafik Sinir A\u011f\u0131 (GNN), d\u00fc\u011f\u00fcmler, kenarlar veya grafi\u011fin tamam\u0131 hakk\u0131nda tahminlerde bulunmak i\u00e7in verilerin grafik yap\u0131s\u0131ndan yararlan\u0131r. Temelde, GNN&#039;ler her bir d\u00fc\u011f\u00fcm\u00fcn \u00f6zelliklerini ve kom\u015fular\u0131n\u0131n \u00f6zelliklerini, mesaj aktarma ve toplama yoluyla d\u00fc\u011f\u00fcm\u00fcn \u00f6zelli\u011fini g\u00fcncellemek i\u00e7in girdi olarak ele al\u0131r. Bu s\u00fcre\u00e7 genellikle GNN&#039;nin &quot;katmanlar\u0131&quot; olarak adland\u0131r\u0131lan birka\u00e7 yineleme i\u00e7in tekrarlan\u0131r ve bilginin a\u011f boyunca yay\u0131lmas\u0131na izin verir.<\/p>\n<h2>Grafik Sinir A\u011flar\u0131n\u0131n \u0130\u00e7 Yap\u0131s\u0131<\/h2>\n<p>GNN mimarisi birka\u00e7 temel bile\u015fenden olu\u015fur:<\/p>\n<ol>\n<li>D\u00fc\u011f\u00fcm \u00f6zellikleri: Grafikteki her d\u00fc\u011f\u00fcm, ger\u00e7ek d\u00fcnya verilerine veya rastgele girdilere dayal\u0131 olabilecek ba\u015flang\u0131\u00e7 \u00f6zelliklerini i\u00e7erir.<\/li>\n<li>Kenar \u00f6zellikleri: Bir\u00e7ok GNN, d\u00fc\u011f\u00fcmler aras\u0131ndaki ili\u015fkileri temsil eden kenarlardan gelen \u00f6zellikleri de kullan\u0131r.<\/li>\n<li>Mesaj aktar\u0131m\u0131: D\u00fc\u011f\u00fcmler, \u00f6zelliklerini g\u00fcncellemek i\u00e7in kom\u015fular\u0131ndan bilgi toplar ve &quot;mesajlar\u0131&quot; grafik boyunca etkili bir \u015fekilde iletir.<\/li>\n<li>Okuma i\u015flevi: Birka\u00e7 bilgi yay\u0131l\u0131m katman\u0131ndan sonra, grafik d\u00fczeyinde bir \u00e7\u0131kt\u0131 olu\u015fturmak i\u00e7in bir okuma i\u015flevi uygulanabilir.<\/li>\n<\/ol>\n<h2>Grafik Sinir A\u011flar\u0131n\u0131n Temel \u00d6zellikleri<\/h2>\n<ul>\n<li><strong>D\u00fczensiz Verileri \u0130\u015fleme Yetene\u011fi:<\/strong> GNN&#039;ler, varl\u0131klar aras\u0131ndaki ili\u015fkilerin \u00f6nemli oldu\u011fu ve geleneksel sinir a\u011flar\u0131 taraf\u0131ndan kolayca yakalanamad\u0131\u011f\u0131 d\u00fczensiz verilerle ba\u015f etme konusunda uzmand\u0131r.<\/li>\n<li><strong>Genellenebilirlik:<\/strong> GNN&#039;ler grafik olarak g\u00f6sterilebilecek herhangi bir soruna uygulanabilir, bu da onlar\u0131 son derece \u00e7ok y\u00f6nl\u00fc k\u0131lar.<\/li>\n<li><strong>Giri\u015f S\u0131ras\u0131na G\u00f6re De\u011fi\u015fmezlik:<\/strong> GNN&#039;ler, grafikteki d\u00fc\u011f\u00fcmlerin s\u0131ras\u0131na bak\u0131lmaks\u0131z\u0131n de\u011fi\u015fmez \u00e7\u0131kt\u0131lar sa\u011flayarak tutarl\u0131 performans sa\u011flar.<\/li>\n<li><strong>Yerel ve K\u00fcresel Kal\u0131plar\u0131 Yakalama Yetene\u011fi:<\/strong> GNN&#039;ler, benzersiz mimarileri sayesinde verilerdeki hem yerel hem de k\u00fcresel kal\u0131plar\u0131 \u00e7\u0131karabilir.<\/li>\n<\/ul>\n<h2>Grafik Sinir A\u011flar\u0131n\u0131n T\u00fcrleri<\/h2>\n<table>\n<thead>\n<tr>\n<th>GNN T\u00fcr\u00fc<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Grafik Evri\u015fimli A\u011flar (GCN&#039;ler)<\/td>\n<td>Mahalle bilgilerini toplamak i\u00e7in bir evri\u015fim i\u015flemi kullan\u0131n.<\/td>\n<\/tr>\n<tr>\n<td>Grafik Dikkat A\u011flar\u0131 (GAT&#039;ler)<\/td>\n<td>Kom\u015fu d\u00fc\u011f\u00fcmlerin etkisini a\u011f\u0131rl\u0131kland\u0131rmak i\u00e7in dikkat mekanizmalar\u0131n\u0131 uygulay\u0131n.<\/td>\n<\/tr>\n<tr>\n<td>Grafik \u0130zomorfizm A\u011flar\u0131 (GIN&#039;ler)<\/td>\n<td>Farkl\u0131 grafik yap\u0131lar\u0131n\u0131 ay\u0131rt ederek farkl\u0131 topolojik bilgileri yakalamak i\u00e7in tasarlanm\u0131\u015ft\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>GrafikSAGE<\/td>\n<td>G\u00f6r\u00fcnmeyen veriler i\u00e7in tahmin yap\u0131lmas\u0131na olanak tan\u0131yan t\u00fcmevar\u0131msal d\u00fc\u011f\u00fcm yerle\u015ftirmelerini \u00f6\u011frenin.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Grafik Sinir A\u011flar\u0131n\u0131n Uygulamalar\u0131 ve Zorluklar\u0131<\/h2>\n<p>GNN&#039;lerin sosyal a\u011f analizi ve biyoinformatikten trafik tahmini ve program do\u011frulamaya kadar \u00e7e\u015fitli uygulamalar\u0131 vard\u0131r. Ancak ayn\u0131 zamanda zorluklarla da kar\u015f\u0131 kar\u015f\u0131yalar. \u00d6rne\u011fin, GNN&#039;ler b\u00fcy\u00fck grafiklere \u00f6l\u00e7eklenebilirlik konusunda zorluk ya\u015fayabilir ve uygun grafik temsilinin tasarlanmas\u0131 karma\u015f\u0131k olabilir.<\/p>\n<p>Bu zorluklar\u0131n \u00fcstesinden gelmek \u00e7o\u011fu zaman do\u011fruluk ve hesaplama verimlili\u011fi aras\u0131ndaki dengeyi gerektirir ve dikkatli tasar\u0131m ve deneme gerektirir. PyTorch Geometric, DGL ve Spektral gibi \u00e7e\u015fitli k\u00fct\u00fcphaneler uygulama ve deneme s\u00fcrecini kolayla\u015ft\u0131rabilir.<\/p>\n<h2>Di\u011fer Sinir A\u011flar\u0131yla Kar\u015f\u0131la\u015ft\u0131rma<\/h2>\n<table>\n<thead>\n<tr>\n<th>Bak\u0131\u015f a\u00e7\u0131s\u0131<\/th>\n<th>GNN&#039;ler<\/th>\n<th>CNN&#039;ler<\/th>\n<th>RNN&#039;ler<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Veri yap\u0131s\u0131<\/td>\n<td>Grafikler<\/td>\n<td>Izgaralar (\u00f6rne\u011fin resimler)<\/td>\n<td>Diziler (\u00f6rne\u011fin metin)<\/td>\n<\/tr>\n<tr>\n<td>Anahtar \u00f6zellik<\/td>\n<td>Grafik yap\u0131s\u0131ndan yararlan\u0131r<\/td>\n<td>Mekansal yerellikten yararlan\u0131r<\/td>\n<td>Zamansal dinamiklerden yararlan\u0131r<\/td>\n<\/tr>\n<tr>\n<td>Uygulamalar<\/td>\n<td>Sosyal a\u011f analizi, molek\u00fcler yap\u0131 analizi<\/td>\n<td>G\u00f6r\u00fcnt\u00fc tan\u0131ma, video analizi<\/td>\n<td>Dil modelleme, zaman serisi analizi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Grafik Sinir A\u011flar\u0131 i\u00e7in Gelecek Perspektifleri ve Teknolojiler<\/h2>\n<p>GNN&#039;ler, daha fazla ara\u015ft\u0131rma ve iyile\u015ftirme i\u00e7in muazzam potansiyele sahip, b\u00fcy\u00fcyen bir alan\u0131 temsil etmektedir. Gelecekteki geli\u015fmeler aras\u0131nda dinamik grafiklerin kullan\u0131lmas\u0131, 3 boyutlu grafiklerin ke\u015ffedilmesi ve daha verimli e\u011fitim y\u00f6ntemlerinin geli\u015ftirilmesi yer alabilir. GNN&#039;lerin takviyeli \u00f6\u011frenme ve transfer \u00f6\u011frenimi ile birle\u015fimi ayn\u0131 zamanda umut verici ara\u015ft\u0131rma yollar\u0131 da sunmaktad\u0131r.<\/p>\n<h2>Grafik Sinir A\u011flar\u0131 ve Proxy Sunucular\u0131<\/h2>\n<p>Proxy sunucular\u0131n\u0131n kullan\u0131m\u0131 dolayl\u0131 olarak GNN&#039;lerin \u00e7al\u0131\u015fmas\u0131n\u0131 destekleyebilir. \u00d6rne\u011fin, \u00e7e\u015fitli \u00e7evrimi\u00e7i kaynaklardan veri toplamay\u0131 i\u00e7eren ger\u00e7ek d\u00fcnya uygulamalar\u0131nda (\u00f6rne\u011fin, sosyal a\u011f analizi i\u00e7in web kaz\u0131ma), proxy sunucular, potansiyel olarak grafik veri k\u00fcmelerinin olu\u015fturulmas\u0131na ve g\u00fcncellenmesine yard\u0131mc\u0131 olarak verimli ve anonim veri toplamaya yard\u0131mc\u0131 olabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ol>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/9046288\" target=\"_new\" rel=\"noopener nofollow\">Grafik Sinir A\u011flar\u0131 \u00dczerine Kapsaml\u0131 Bir Ara\u015ft\u0131rma<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1812.08434\" target=\"_new\" rel=\"noopener nofollow\">Grafik Sinir A\u011flar\u0131: Y\u00f6ntem ve Uygulamalar\u0131n G\u00f6zden Ge\u00e7irilmesi<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1812.04202\" target=\"_new\" rel=\"noopener nofollow\">Grafiklerde Derin \u00d6\u011frenme: Bir Ara\u015ft\u0131rma<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/rusty1s\/pytorch_geometric\" target=\"_new\" rel=\"noopener nofollow\">PyTorch Geometrik K\u00fct\u00fcphanesi<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468487,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477375","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Graph Neural Networks: Harnessing Power from Graph-Structured Data<\/mark>","faq_items":[{"question":"What are Graph Neural Networks (GNNs)?","answer":"<p>Graph Neural Networks (GNNs) are a type of neural network designed to process and make predictions about data structured as a graph. They are particularly useful in problems where the relationships between entities are complex and cannot be efficiently captured by traditional neural networks.<\/p>"},{"question":"When was the concept of GNNs first introduced?","answer":"<p>The concept of Graph Neural Networks first emerged in the early 2000s with the work of Franco Scarselli, Marco Gori, and others. They laid the groundwork for future development of GNNs.<\/p>"},{"question":"How do GNNs work?","answer":"<p>GNNs operate by treating each node's features and its neighbors' features as inputs to update the node's feature through a process called message passing and aggregation. This process is often repeated for several iterations or \"layers\", which allows information to propagate through the network.<\/p>"},{"question":"What are some key features of GNNs?","answer":"<p>Key features of GNNs include their capability to handle irregular data, generalizability to any problem that can be represented as a graph, invariance to input order, and their ability to capture both local and global patterns in the data.<\/p>"},{"question":"What types of Graph Neural Networks exist?","answer":"<p>Several types of Graph Neural Networks exist, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Isomorphism Networks (GINs), and GraphSAGE.<\/p>"},{"question":"What are some applications of GNNs and what challenges do they face?","answer":"<p>Applications of GNNs are diverse and include social network analysis, bioinformatics, traffic prediction, and program verification. However, they do face challenges like scalability to large graphs and complexity in designing the appropriate graph representation.<\/p>"},{"question":"How do GNNs compare with other neural networks?","answer":"<p>Unlike Convolutional Neural Networks (CNNs) that exploit spatial locality in grid-like data (like images), and Recurrent Neural Networks (RNNs) that exploit temporal dynamics in sequential data (like text), GNNs exploit the graph structure in the data.<\/p>"},{"question":"What is the future of GNNs?","answer":"<p>The field of GNNs is rapidly growing, with potential for further exploration and improvement. Future developments may include handling dynamic graphs, exploring 3D graphs, and developing more efficient training methods.<\/p>"},{"question":"How can proxy servers be used with Graph Neural Networks?","answer":"<p>Proxy servers can indirectly support the operation of GNNs. In real-world applications like data collection from various online sources, proxy servers can assist in efficient and anonymous data collection, thereby aiding in the construction and updating of graph datasets.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477375\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468487"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}