Graph Models

Graphs • 62 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 565
2015 208
2017 80
2017 49
2020 37
2017 24
2018 21
2020 20
2018 20
2019 20
2018 19
2016 17
2017 11
2017 9
2015 9
2018 6
2018 6
2020 4
2020 4
2016 4
2018 4
2018 4
2019 3
2020 3
2020 3
2019 2
2020 2
2019 2
2020 2
2020 2
2018 2
2018 2
2018 2
2020 1
2020 1
2020 1
2020 1
2020 1
2021 1
2020 1
2020 1
2020 1
2020 1
2021 1
2021 1
2021 1
2021 1
2020 1
2021 1
2017 1
2018 1
2018 1
2017 1
2018 1
2019 1
2019 1
2010 0
2009 0