Results 21 to 30 of about 437,099 (249)
Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 more
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Neural Networks, 2020 Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs.
Giannis Nikolentzos +2 more
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Framework and Algorithms for Accelerating Training of Semi-supervised Graph Neural Network Based on Heuristic Coarsening Algorithms [PDF]
Jisuanji kexueGraph neural network is the mainstream tool of graph machine learning at the current stage,and it has broad development prospects.By constructing an abstract graph structure,the graph neural network model can be used to efficiently deal with problems in ...
CHEN Yufeng , HUANG Zengfeng
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Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
Mathematics, 2022 Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li +3 more
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Graph Rewriting for Graph Neural Networks
, 2023 Originally submitted to ICGT 2023, part of STAF ...
Machowczyk, Adam, Heckel, Reiko
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Non-Local Graph Neural Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 8 pages, 2 figures, accepted by ...
Meng Liu, Zhengyang Wang, Shuiwang Ji
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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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Benchmarking Graph Neural Networks
, 2020 Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Dwivedi, Vijay Prakash +5 more
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Bilinear Graph Neural Network with Neighbor Interactions
, 2020 Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the ...
Feng, Fuli +6 more
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Graph Convolutional Neural Network [PDF]
, 2016 The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification.
Xianghua Xie
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