ÇİZGE ÖĞRENMEDE ÇİZGE SİNİR AĞLARI

Öz

Sinir ağları, alt alanlarında geniş bir aileyi sahiptir. Sinir ağlarının kendi içerisinde karşılaştığı kısıtlar ve limitler gelişimini olumlu yönde etkilemiş ve yeni sinir ağı modellerinin oluşmasını sağlamıştır. Bunun en büyük örneği bazı üç boyutlu verilerde yüksek başarı sergilemeyen evrişimli sinir ağı (Convolutional Neural Network-CNN) modellerine ek olarak çizge sinir ağı (Graph Neural Network-GNN) modellerinin geliştirilmesi olmuştur. Bir derin öğrenme modeli olan GNN, temelde çizge öğrenmeyi kullanmaktadır. GNN’ler bir nevi çizge derin öğrenmedir. Ancak bilinmelidir ki GNN’ler sinir ağları ailesinin bir üyesi olduğu gibi çizge öğrenmenin de alt modellerinden birisidir. Bu çalışmada çizge öğrenme ve GNN’ler ile ilgili temel kavramlar, ortak özellikler, farklılıklar, avantajlar, dezavantajlar ve uygulama alanlarından bahsedilmektedir.

Anahtar Kelimeler

• Çizge Sinir Ağları
• Çizge Öğrenme
• Yapay Sinir Ağları
• Derin Öğrenme

GRAPH NEURAL NETWORKS IN GRAPH LEARNING

Öz

Neural networks have a large family of sub-fields. The constraints and limitations that neural networks face within themselves have positively affected their development and led to the creation of new neural network models. The biggest example of this is the development of graph neural network (GNN) models in addition to convolutional neural network (CNN) models, which do not perform well in some three-dimensional data. GNN, a deep learning model, basically uses graph learning. GNNs are a kind of graph deep learning. However, it should be noted that GNNs are a member of the neural network family and a sub-model of graph learning. In this study, the basic concepts, common features, differences, advantages, disadvantages and application areas of graph learning and GNNs are discussed.

Anahtar Kelimeler

• Graph Neural Networks
• Graph Learning
• Artificial Neural Networks
• Deep Learning

Kaynakça

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