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Journals / Savunma Bilimleri Dergisi / 2026 / Cilt: 20 Sayı: 1

A Review on Graph Neural Networks

Grafik Sinir Ağları Üzerine Bir İnceleme

Hamza Talha Gümüş(MİLLİ SAVUNMA ÜNİVERSİTESİ)0000-0001-7360-8138
Can Eyüpoğlu(HAVA HARP OKULU KOMUTANLIĞI, HAVA HARP OKULU (DEKANLIK), BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-6133-8617
Pages
105–138
Publication Date
2024-05-07
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, 65 sub-models created on the basis of GNN and GCN models and other models affected by these models were examined.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:In this study, 65 sub-models created on the basis of GNN and GCN models and other models affected by these models were examined.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Grafik Sinir Ağları · Yapay Sinir Ağları · Derin Öğrenme

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.

Özet

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.

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Cite This Article

Gümüş, H. T., Eyüpoğlu, C. (2024). A Review on Graph Neural Networks. Savunma Bilimleri Dergisi, Cilt: 20 Sayı: 1, 105–138. https://doi.org/10.17134/khosbd.1284174

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