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 |
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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 | |

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2020 | 4 | |

2016 | 4 | |

2018 | 4 | |

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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 |