Graph neural networks by Lian, Ran

“…In recent years, Graph Neural Networks (GNNs) have become increasingly attractive methods for molecular property prediction due to their abilities to analyze graph structural data with chemical structures being easily displayed as graphs. …”
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Schatten Graph Neural Networks by Youfa Liu, Yongyong Chen, Guo Chen, Jiawei Zhang

“…Recalling recent works on graph neural networks, we found that imposing graph smoothing via Frobenius norm was proven to be effective in the architecture of graph neural networks from the standpoint of the graph signal processing. …”
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Federated graph neural network by Koh, Tat You @ Arthur

“…Graph Neural Networks is a form of machine learning that has seen significant growth in popularity and use, owing to their natural affinity for capturing implicit representations that exist in real-world phenomena. …”
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Mathematical Expressiveness of Graph Neural Networks by Guillaume Lachaud, Patricia Conde-Cespedes, Maria Trocan

Subjects: “…graph neural networks…”
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Evaluating explainability for graph neural networks by Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik

“…Abstract As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. …”
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Graph neural network with knowledge graph by Ang, Qi Xuan

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Federated learning for graph neural networks by Yan, Yige

“…This dissertation investigates the combination of graph neural networks (GNNs) and federated learning (FL) for addressing practical problems while preserving data privacy and reducing computational complexity. …”
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A study on graph neural networks by Choo, Patricia Yu Wei

“…This report investigates various Graph Neural Network (GNN) models and its performance and stability. …”
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Graph neural network for anomaly detection by Yeo, Ming Hong

“…Graph Neural Networks (GNNs) have gained prominence in the realm of anomaly detection on graph-structured data, a critical task in various fields such as cybersecurity, fraud detection, and network monitoring. …”
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Graph neural networks for questions and answers by Mah, Caleb

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Graph neural networks for network analysis by He, Y

“…<p>With an increasing number of applications where data can be represented as graphs, graph neural networks (GNNs) are a useful tool to apply deep learning to graph data. …”

Orbit-equivariant graph neural networks by Morris, M, Grau, BC, Horrocks, I

“…Equivariance is an important structural property that is captured by architectures such as graph neural networks (GNNs). However, equivariant graph functions cannot produce different outputs for similar nodes, which may be undesirable when the function is trying to optimize some global graph property. …”

GNNGLY: Graph Neural Networks for Glycan Classification by Alhasan Alkuhlani, Walaa Gad, Mohamed Roushdy, Abdel-Badeeh M. Salem

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Quantized Graph Neural Networks for Image Classification by Xinbiao Xu, Liyan Ma, Tieyong Zeng, Qinghua Huang

Subjects: “…graph neural network…”
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Hierarchical Model Selection for Graph Neural Networks by Yuga Oishi, Ken Kaneiwa

Subjects: “…Graph neural networks (GNNs)…”
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On the distribution alignment of propagation in graph neural networks by Qinkai Zheng, Xiao Xia, Kun Zhang, Evgeny Kharlamov, Yuxiao Dong

Subjects: “…Graph neural networks…”
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Degree-Aware Graph Neural Network Quantization by Ziqin Fan, Xi Jin

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Graph neural networks for materials science and chemistry by Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

“…Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. …”
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CN-Motifs Perceptive Graph Neural Networks by Fan Zhang, Tian-Ming Bu

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Graph Neural Networks for Maximum Constraint Satisfaction by Jan Tönshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe

Subjects: “…graph neural networks…”
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