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Simple and Efficient Heterogeneous Graph Neural Network [PDF]
AAAI Conference on Artificial Intelligence, 2022 Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
Xiaocheng Yang +4 more
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Prototype-based Interpretable Graph Neural Networks [PDF]
, 2022 Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks.
Biagio La Rosa +2 more
core +1 more source
Frontiers in Cell and Developmental Biology, 2021 Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight.
Vidya Manian +2 more
doaj +1 more source
Graph Neural Networks: A Review of Methods and Applications [PDF]
AI Open, 2018 Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require a model to learn from
Jie Zhou +5 more
semanticscholar +1 more source
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation [PDF]
Web Search and Data Mining, 2022 Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
Liangwei Yang +6 more
semanticscholar +1 more source
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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Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
IEEE Signal Processing Magazine, 2020 Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure.
Fernando Gama +3 more
openaire +3 more sources
J. Mach. Learn. Res., 2023 In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs.
Zhen Zhang 0008 +4 more
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Intelligent prediction method of network performance based on graph neural network
Dianxin kexue, 2022 There are some problems in the traditional network performance prediction technology, such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and ...
Yijiang LI +5 more
doaj +2 more sources
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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