Results 31 to 40 of about 38,896 (265)
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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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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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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Aspect-based Sentiment Analysis Based on Dual-channel Graph Convolutional Network with Sentiment Knowledge [PDF]
Jisuanji kexue, 2023 Aspect-based sentiment analysis is a fine-grained sentiment analysis task whose goal is to classify the sentiment polarity of the given aspect terms in a sentence.Most of the current sentiment classification models build a graph neural network on the ...
YANG Ying, ZHANG Fan, LI Tianrui
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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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A Hyperbolic-to-Hyperbolic Graph Convolutional Network [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 CVPR2021 ...
Jindou Dai +3 more
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PLoS ONE, 2021 Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
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Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
IEEE Access, 2020 Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li +4 more
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Semantic–Structural Graph Convolutional Networks for Whole-Body Human Pose Estimation
Information, 2022 Existing whole-body human pose estimation methods mostly segment the parts of the body’s hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the complexity of ...
Weiwei Li, Rong Du, Shudong Chen
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Generative Graph Convolutional Network for Growing Graphs [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nodes without nontrivial ...
Da Xu +5 more
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