Results 31 to 40 of about 145,055 (276)
A deep graph convolutional neural network architecture for graph classification.
PLoS ONE, 2023 Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to
Yuchen Zhou +3 more
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Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
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Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Directed Graph Convolutional Network
CoRR, 2020 Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their application scope. In this paper, we extend spectral-based graph convolution to directed graphs by using first- and second-order proximity, which can not only retain the ...
Zekun Tong +4 more
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Masked Graph Convolutional Network [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph which is usually constructed from the data features, while the graph convolutional neural networks smooth ...
Liang Yang 0002 +4 more
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Spatial Graph Convolutional Networks [PDF]
, 2020 Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an ...
Tomasz Danel +6 more
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Graph Convolutional Networks for Road Networks [PDF]
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019 Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments.
Tobias Skovgaard Jepsen +2 more
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Simplifying Graph Convolutional Networks
CoRR, 2019 Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess
Felix Wu +5 more
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Graph Convolutional Network Adversarial Attack Method for Brain Disease Diagnosis [PDF]
Jisuanji kexue, 2022 In recent years,brain functional networks analysis using the resting state functional magnetic resonance imaging data has been widely used in computer-aided diagnosis tasks of various brain diseases.The graph convolutional network framework integrating ...
WANG Xiao-ming, WEN Xu-yun, XU Meng-ting, ZHANG Dao-qiang
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Semantic Graph Convolutional Networks for 3D Human Pose Regression
, 2020 In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node.
Kapadia, Mubbasir +4 more
core +1 more source