Grafik Sinir Ağları Üzerine Bir İnceleme

Year 2024, Volume: 20 Issue: 1, 105 - 138, 07.05.2024

Hamza Talha Gümüş Can Eyüpoğlu

https://doi.org/10.17134/khosbd.1284174

Abstract

Grafik sinir ağları (Graph Neural Networks-GNN), yapay sinir ağları (Artificial Neural Networks-ANN) ailesine mensup ve grafikler üzerinden bilgi çıkarımı işlemi gerçekleştiren bir derin öğrenme yöntemidir. Bilinen ilk modeller GNN tabanlı oluşturulsa da evrişimli ağların kullanımının yaygınlaşması ile grafik evrişimli ağ (Graph Convolutional Network-GCN) modeli de popülerlik kazanmıştır. Bu durum GNN’lerin gelişmesine katkı sağlarken, iki ağ tabanında yeni modeller oluşturulmasına öncül olmuştur. Bu çalışmada GNN ve GCN modelleri tabanında oluşturulmuş 65 alt model ve bu modellerin etkilediği diğer modeller incelenmiştir.

Keywords

Grafik Sinir Ağları, Yapay Sinir Ağları, Derin Öğrenme, Grafik Evrişimli Ağlar

Thanks

Bu çalışma Milli Savunma Üniversitesi, Atatürk Stratejik Araştırmalar ve Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı, Siber Güvenlik Tezsiz Yüksek Lisans Programına bağlı olarak yürütülen “Siber Güvenlikte Grafik Sinir Ağları” adlı dönem projesinin bir bölümüdür.

A Review on Graph Neural Networks

Abstract

Graph neural networks (GNN) are a deep learning method that belongs to the family of artificial neural networks (ANN) and performs information extraction over graphs. Although the first known models were created based on GNN, the Graph Convolutional Network (GCN) model has gained popularity with the widespread use of convolutional networks. While this has contributed to the development of GNNs, it led to the creation of new models on two network bases. In this study, 65 sub-models created on the basis of GNN and GCN models and other models affected by these models were examined.

Keywords

Graph Neural Networks, Artificial Neural Networks, Deep Learning, Graph Convolutional Networks

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