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Graph Neural Networks have emerged as a useful tool to learn on the data by applying additional constraints based on the graph structure.
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“Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering”
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“Fast Approximations of betweenness centrality using Graph Neural Networks”, 2019
Sunil. Maurya et al · 2019
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Thomas. Kipf et al · 2017
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“Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling”
Diego Marcheggiani et al · 2017
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“Graph Attention Networks”
Petar Velickovic et al · 2017
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“Adaptive Diffusions for Scalable Learning Over Graphs” Conference Name: IEEE Transactions on Signal Processing
D. Berberidis et al · 2018
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“FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling”
Jie Chen et al · 2018
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Johannes Klicpera et al · 2018
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“Representation Learning on Graphs with Jumping Knowledge Networks” ISSN: 2640-3498
Keyulu Xu et al · 2018
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“Simple and Deep Graph Convolutional Networks”
M. Chen et al · 2020
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“Geom-GCN: Geometric Graph Convolutional Networks”
Hongbin Pei et al · 2020
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“DropEdge: Towards Deep Graph Convolutional Networks on Node Classification”
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“Microsoft Academic Graph: When experts are not enough” Publisher: MIT Press
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“Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs”
Jiong Zhu et al · 2020
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