Results 21 to 30 of about 5,698,498 (295)
Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms [PDF]
, 2023 In response to the challenges of learning confusion and information overload in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data ...
3, Yuyi, Wang, SaiNan, 1, Bozhi
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Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
Mathematics, 2023 Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets.
Anna Boronina +2 more
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Tangent Graph Convolutional Network [PDF]
, 2021 Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors.
Luca Pasa +2 more
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Hybrid Graph Models for Traffic Prediction
Applied Sciences, 2023 Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions.
Renyi Chen, Huaxiong Yao
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Graph matching as a graph convolution operator for graph neural networks [PDF]
Pattern Recognition Letters, 2021 Abstract Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are defined on euclidean spaces.
Martineau, Maxime +3 more
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Multipath Graph Convolutional Neural Networks
CoRR, 2021 Las redes de convolución de gráficos han atraído recientemente mucha atención para el aprendizaje de la representación en espacios de características no euclidianos. Investigaciones recientes se han centrado en el apilamiento de múltiples capas como en las redes neuronales convolucionales para el aumento del poder expresivo de las redes de convolución ...
Rangan Das +3 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Adaptive filters in Graph Convolutional Neural Networks
Pattern Recognition, 2023 Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data.
Andrea Apicella +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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Transformer-Based Graph Convolutional Network for Sentiment Analysis
Applied Sciences, 2022 Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years.
Barakat AlBadani +4 more
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