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 852
2015 259
2020 205
2017 149
2017 96
2017 58
2018 40
2020 36
2018 33
2018 33
2019 31
2016 23
2017 21
2020 16
2017 14
2020 11
2015 9
2019 8
2018 8
2018 8
2020 7
2021 6
2018 6
2018 5
2020 4
2016 4
2018 4
2020 3
2020 3
2020 3
2020 3
2018 3
2019 3
2019 2
2020 2
2019 2
2020 2
2018 2
2022 2
2020 1
2020 1
2020 1
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
2019 1
2022 1
2022 1
2009 0