Results 21 to 30 of about 110,849 (310)
Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Molecules, 2022 Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological
Hang Zhou +4 more
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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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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 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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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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Graph-Informed Neural Networks for Regressions on Graph-Structured Data
Mathematics, 2022 In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN).
Stefano Berrone +4 more
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Explainable Graph Neural Networks for Organic Cages
, 2021 The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science.
Qi, Yuan +2 more
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Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
IEEE Signal Processing Magazine, 2020 Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure.
Fernando Gama +3 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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