Graph Attention Network (GAT) Nedir? (Dikkat Mekanizmalı Çizge Sinir Ağı)

[{"title": "Graph Attention Network (GAT) Nedir?", "content": "Graph Attention Network (GAT), \u00e7izge (graf) yap\u0131l\u0131 verilere dikkat mekanizmas\u0131 (attention mechanism) uygulayan bir yapay sinir a\u011f\u0131 mimarisidir. Velickovic ve ark. taraf\u0131ndan 2018'de \u00f6nerilen GAT, bir d\u00fc\u011f\u00fcm\u00fcn temsilini g\u00fcncellerken kom\u015fu d\u00fc\u011f\u00fcmlere sabit a\u011f\u0131rl\u0131k atamak yerine, her kom\u015fu i\u00e7in ayr\u0131 dikkat puanlar\u0131 hesaplayarak hangi kom\u015fular\u0131n daha \u00f6nemli oldu\u011funu \u00f6\u011frenir."}, {"title": "Nas\u0131l \u00c7al\u0131\u015f\u0131r?", "content": "GAT, bir d\u00fc\u011f\u00fcm ile her kom\u015fusu aras\u0131nda dikkat katsay\u0131s\u0131 (attention coefficient) hesaplar. Bu katsay\u0131lar Leaky ReLU aktivasyonlu k\u00fc\u00e7\u00fck bir sinir a\u011f\u0131yla \u00fcretilir ve softmax ile normalize edilir. Son d\u00fc\u011f\u00fcm temsili, kom\u015fu \u00f6zelliklerinin bu a\u011f\u0131rl\u0131kl\u0131 toplam\u0131ndan olu\u015fur. Kararl\u0131l\u0131k ve farkl\u0131 ili\u015fki \u00f6r\u00fcnt\u00fclerini yakalamak i\u00e7in \u00e7ok ba\u015fl\u0131kl\u0131 dikkat (multi-head attention) kullan\u0131l\u0131r; ara katmanlarda ba\u015fl\u0131k \u00e7\u0131kt\u0131lar\u0131 birle\u015ftirilir, \u00e7\u0131k\u0131\u015f katman\u0131nda ortalamas\u0131 al\u0131n\u0131r."}, {"title": "GCN ve GNN'den Fark\u0131", "content": "\u00c7izge Evri\u015fimsel A\u011f (GCN), t\u00fcm kom\u015fulara e\u015fit a\u011f\u0131rl\u0131k atarken GAT her kom\u015funun \u00f6nem derecesini veri odakl\u0131 bi\u00e7imde \u00f6\u011frenir. Bu esneklik, GAT'\u0131 de\u011fi\u015fken b\u00fcy\u00fckl\u00fckteki kom\u015fuluk yap\u0131lar\u0131nda ve karma\u015f\u0131k ili\u015fkilerde \u00e7ok daha ifade g\u00fcc\u00fc y\u00fcksek k\u0131lar. GNN (Graph Neural Network) ise bu t\u00fcr t\u00fcm \u00e7izge sinir a\u011flar\u0131n\u0131 kapsayan \u00fcst terimdir; GCN ve GAT, GNN'nin \u00f6zel t\u00fcrleridir."}, {"title": "Uygulama Alanlar\u0131", "content": "GAT; d\u00fc\u011f\u00fcm s\u0131n\u0131fland\u0131rma (node classification), ba\u011flant\u0131 tahmini (link prediction) ve \u00e7izge s\u0131n\u0131fland\u0131rma gibi temel \u00e7izge g\u00f6revlerinde kullan\u0131l\u0131r. Pratik uygulamalar aras\u0131nda sosyal a\u011f analizi, ila\u00e7-protein etkile\u015fim tahmini, molek\u00fcler biyoloji, bilgi grafi\u011fi tamamlama ve i\u00e7erik tabanl\u0131 \u00f6neri sistemleri yer al\u0131r."}, {"title": "Avantajlar\u0131", "content": "GAT, dikkat katsay\u0131lar\u0131n\u0131 kom\u015fular aras\u0131nda paralel hesaplad\u0131\u011f\u0131 i\u00e7in verimlidir ve farkl\u0131 b\u00fcy\u00fckl\u00fckteki kom\u015fuluklara do\u011fal uyum sa\u011flar. \u00dcretilen dikkat a\u011f\u0131rl\u0131klar\u0131 yorumlanabilir (interpretable) oldu\u011fundan hangi kom\u015fular\u0131n etkin oldu\u011fu analiz edilebilir. Birden fazla ili\u015fki t\u00fcr\u00fc i\u00e7eren heterojen \u00e7izgeler i\u00e7in de geni\u015fletilebilir."}]