Results 31 to 40 of about 5,698,498 (295)
Pooling in Graph Convolutional Neural Networks [PDF]
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 5 pages, 2 figures, 2019 Asilomar Conference ...
Mark Cheung +4 more
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Future Internet, 2021 Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders.
Ibsa K. Jalata +4 more
doaj +1 more source
Exact combinatorial optimization with graph convolutional neural networks [PDF]
, 2019 Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable ...
Lodi A. +4 more
core +3 more sources
Survey of Graph Neural Network in Recommendation System [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well.
WU Jing, XIE Hui, JIANG Huowen
doaj +1 more source
A Convolutional Neural Network into graph space
CoRR, 2020 arXiv admin note: text overlap with arXiv:1611.08402 by other ...
Maxime Martineau +3 more
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A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting
, 2020 Forecasting the traffic flow is a critical issue for researchers and practitioners in the field of transportation. Using the graph convolutional network (GCN) is widespread in traffic flow forecasting. Existing GCN-based methods mostly rely on undirected
Zhaoyang Li +7 more
core +1 more source
Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]
Jisuanji kexueGraph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
doaj +1 more source
Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
Applied Sciences, 2023 In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to ...
Yifei Wang +4 more
doaj +1 more source
Anomaly detection with convolutional Graph Neural Networks [PDF]
Journal of High Energy Physics, 2021 Abstract We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based
Atkinson, Oliver +4 more
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Adaptive Graph Convolutional Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity.
Ruoyu Li 0002 +3 more
openaire +4 more sources