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A Comprehensive Survey on Graph Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding.
Chengqi Zhang, Philip Yu, Shirui Pan
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Learning Graph Neural Networks with Positive and Unlabeled Nodes [PDF]
ACM Transactions on Knowledge Discovery From Data, 2021 Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes.
Lan Du, Shirui Pan, Xingquan Zhu
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Computational Capabilities of Graph Neural Networks
IEEE Transactions on Neural Networks, 2009 In this paper, we will consider the universal approximation properties of a recently introduced neural network model called graph neural network (GNN) which can be used to process structured data inputs, e.g.
Markus Hagenbuchner +2 more
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Graph Mining with Graph Neural Networks
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021Graphs are ubiquitous data structures in various fields, such as social media, transportation, linguistics and chemistry. To solve downstream graph-related tasks, it is of great significance to learn effective representations for graphs. My research strives to help meet this demand; due to the huge success of deep learning methods, especially graph ...
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Graph Neural Networks in Cheminformatics
2021Graph neural networks represent nowadays the most effective machine learning technology in the biochemistry domain. Learning on the huge amount of chemical data can take an important part in finding new molecules or new drugs, which is a crucial research work in cheminformatics.
H. N. Tran Tran +4 more
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Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks
2023Neural networks that can process the parameters of other neural networks find applications in diverse domains, including processing implicit neural representations, domain adaptation of pretrained networks, generating neural network weights, and predicting generalization errors.
Zhang, D.W. +5 more
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Graph Neural Networks: Architectures, Stability, and Transferability
Proceedings of the IEEE, 2021Fernando Gama +2 more
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Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
ACM Computing Surveys, 2022Akshay Jain, Sergi Abadal
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Graph Neural Networks with Convolutional ARMA Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Lorenzo Livi +2 more
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