Results 21 to 30 of about 4,082,283 (306)
Stochastic Graph Neural Networks [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
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Learning the Network of Graphs for Graph Neural Networks
CoRR, 2022 Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN.
Yixiang Shan +5 more
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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks
2023 IEEE International Conference on Big Data (BigData), 2023 This paper is submitted and will be published on Big Data Conference 2023, Data-driven Science for Graphs: Algorithms, Architectures, and Application ...
Zheyi Qin +2 more
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Advances in Neural Information Processing Systems 35, 2022 We consider the bandit optimization problem with the reward function defined over graph-structured data. This problem has important applications in molecule design and drug discovery, where the reward is naturally invariant to graph permutations. The key challenges in this setting are scaling to large domains, and to graphs with many nodes.
Kassraie, Parnian +2 more
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Graph neural networks for prediction of protein isoelectric points [PDF]
, 2022 Graph neural networks were used to model protein isoelectric points. Predictions contained markedly fewer outliers (predicted with errors > 0.5 pH units) compared to tools published in the literature, despite slightly higher root-mean-squared errors ...
Tom, Brenner
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Graph Clustering with Graph Neural Networks
J. Mach. Learn. Res., 2020 Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Anton Tsitsulin +3 more
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Graph Rewriting for Graph Neural Networks
, 2023 Originally submitted to ICGT 2023, part of STAF ...
Adam Machowczyk, Reiko Heckel
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Learning graph normalization for graph neural networks [PDF]
Neurocomputing, 2022 15 pages, 3 figures, 6 ...
Yihao Chen +4 more
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J. Mach. Learn. Res., 2023 In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs.
Zhen Zhang 0008 +4 more
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Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches [PDF]
, 2023 Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph ...
Lukasikt, S, Krzywda, M, Gandomi, AH
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