Results 51 to 60 of about 3,278,052 (298)
Graph Learning-Convolutional Networks
CoRR, 2018 Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may be not optimal for semi-supervised learning tasks.
Bo Jiang 0002 +3 more
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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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Dual-channel deep graph convolutional neural networks
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
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Text Classification Method Based on Graph Neural Networks [PDF]
ITM Web of ConferencesThe goal of text classification is to assign labels to text units accurately, which is a basic task in natural language processing. This technology has shown great value in many practical application scenarios, covering spam detection, emotional tendency
Gao Ruofei
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Upright Adjustment With Graph Convolutional Networks
2020 IEEE International Conference on Image Processing (ICIP), 2020 We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical ...
Raehyuk Jung, Sungmin Cho, Junseok Kwon
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Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification
, 2021 Recently, graph convolutional network (GCN) has progressed significantly and gained increasing attention in hyperspectral image (HSI) classification due to its impressive representation power.
Wan, Sheng +5 more
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Single-cell classification using graph convolutional networks
BMC Bioinformatics, 2021 Background Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research.
Tianyu Wang, Jun Bai, Sheida Nabavi
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Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
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Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
Applied SciencesThis research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns.
Vandana Singh +2 more
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Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
Applied Sciences, 2022 Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning.
Yang Wang, Shunping Xiao
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