An Overview of Graph Neural Networks

Öz

Graph neural networks (GNN) are a deep learning method that belongs to the artificial neural network (ANN) family and performs information extraction from graphs. It was first used in 2008 and its development started in 2014 and onwards. GNNs, which were developed in response to the failure of convolutional neural networks (CNN) to meet the expected performance on visuals, are used in many fields such as physics, chemistry, biology and cyber security. In this study, GNNs and their models are basically explained and a comprehensive literature review is carried out. In the study, the developed models are examined by referring to the design steps of a GNN model, and the strengths and weaknesses of GNN models against CNN and recurrent neural networks (RNN) effective models are shown.

Anahtar Kelimeler

• Graph Neural Networks
• Artificial Neural Networks
• Deep Learning
• Convolutional Neural Networks
• Recurrent Neural Networks

Grafik Sinir Ağlarına Genel Bir Bakış

Öz

Grafik sinir ağları (GNN), yapay sinir ağı (ANN) ailesine mensup ve grafikler üzerinden bilgi çıkarımı işlemi gerçekleştiren bir derin öğrenme yöntemidir. İlk kullanımı 2008 yılında gerçekleşmiş, gelişimi ise 2014 yılı ve sonrasında olmuştur. Evrişimli sinir ağlarının (CNN) görseller üzerinde beklenen performansı karşılamamasına karşılık olarak geliştirilen GNN’ler; fizik, kimya, biyoloji, siber güvenlik gibi birçok alanda kullanılmaktadır. Bu çalışmada GNN’ler ve modelleri temel olarak anlatılmış ve kapsamlı bir literatür taraması gerçekleştirilmiştir. Çalışma içerisinde bir GNN modelinin tasarım adımlarına değinilerek geliştirilen modeller incelenmiş, GNN modellerinin CNN ve yinelemeli sinir ağları (RNN) etkili modellere karşı güçlü ve zayıf yönleri gösterilmiştir.

Anahtar Kelimeler

• Grafik Sinir Ağları
• Yapay Sinir Ağları
• Derin Öğrenme
• Evrişimli Sinir Ağları
• Yinelemeli Sinir Ağları

Kaynakça

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