Graph Models

Graphs • 66 methods

The Graph Methods include neural network architectures for learning on graphs with prior structure information, popularly called as Graph Neural Networks (GNNs).

Recently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different challenges. Graph neural networks are particularly useful in applications where data are generated from non-Euclidean domains and represented as graphs with complex relationships.

Some tasks where GNNs are widely used include node classification, graph classification, link prediction, and much more.

In the taxonomy presented by Wu et al. (2019), graph neural networks can be divided into four categories: recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks.

Image source: A Comprehensive Survey on Graph NeuralNetworks

Method Year Papers
2016 957
2020 288
2015 274
2017 193
2017 128
2017 65
2018 48
2020 46
2018 41
2018 40
2019 37
2016 25
2017 22
2020 21
2017 20
2020 15
2019 11
2018 10
2015 9
2021 9
2018 8
2020 7
2018 7
2016 6
2020 5
2020 5
2018 5
2020 4
2020 4
2018 4
2020 3
2020 3
2018 3
2019 3
2019 2
2020 2
2019 2
2018 2
2019 2
2020 1
2020 1
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2020 1
2020 1
2020 1
2020 1
2021 1
2021 1
2021 1
2021 1
2021 1
2010 1
2017 1
2018 1
2018 1
2017 1
2018 1
2022 1
2022 1
2022 1
2009 0