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deep_gcns_torch

Python

1.0.0

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DeepGCNS：GCN可以像CNN一样深吗？

在这项工作中，我们提出了成功培训非常深入GCN的新方法。我们从CNN借用概念，主要是残留/密集的连接和扩张的卷积，并将其适应GCN体系结构。通过广泛的实验，我们显示了这些深GCN框架的积极作用。

[Project] [Paper] [幻灯片] [TensorFlow代码] [Pytorch代码]

概述

我们进行了广泛的实验，以显示不同的组件（#layers，#filters，#nearest邻居，扩张等）如何影响DeepGCNs 。我们还提供有关不同类型的深GCN（MRGCN，EDGECONV，GraphSage和Gin）的消融研究。

如何训练，测试和评估我们的模型

请查看examples文件夹中每个任务的Readme.md中的详细信息。代码，数据和验证模型的所有信息都可以在此处找到。

DeepGCNS（ICCV'2019，TPAMI'2021）：S3DIS，PARTNET，MOTFERNET40，PPI
Deepergcn（Arxiv'2020）：OGB
Gnn'1000（ICML'2021）：OGB

建议的要求

Python> = 3.7
pytorch> = 1.9.0
pytorch_ geometric> = 1.6.0
OGB> = 1.3.1仅用于OGB数据集上的实验
DGL> = 0.5.3仅用于实验examples/ogb_eff/ogbn_arxiv_dgl

通过运行安装环境：

 source deepgcn_env_install.sh

代码架构

 .
├── misc                    # Misc images
├── utils                   # Common useful modules
├── gcn_lib                 # gcn library
│   ├── dense               # gcn library for dense data (B x C x N x 1)
│   └── sparse              # gcn library for sparse data (N x C)
├── eff_gcn_modules         # modules for mem efficient gnns
├── examples 
│   ├── modelnet_cls        # code for point clouds classification on ModelNet40
│   ├── sem_seg_dense       # code for point clouds semantic segmentation on S3DIS (data type: dense)
│   ├── sem_seg_sparse      # code for point clouds semantic segmentation on S3DIS (data type: sparse)
│   ├── part_sem_seg        # code for part segmentation on PartNet
│   ├── ppi                 # code for node classification on PPI dataset
│   └── ogb                 # code for node/graph property prediction on OGB datasets
│   └── ogb_eff             # code for node/graph property prediction on OGB datasets with memory efficient GNNs
└── ...

引用

如果您发现任何有用的话，请引用我们的论文

 @InProceedings{li2019deepgcns,
    title={DeepGCNs: Can GCNs Go as Deep as CNNs?},
    author={Guohao Li and Matthias Müller and Ali Thabet and Bernard Ghanem},
    booktitle={The IEEE International Conference on Computer Vision (ICCV)},
    year={2019}
}

 @article{li2021deepgcns_pami,
  title={Deepgcns: Making gcns go as deep as cnns},
  author={Li, Guohao and M{"u}ller, Matthias and Qian, Guocheng and Perez, Itzel Carolina Delgadillo and Abualshour, Abdulellah and Thabet, Ali Kassem and Ghanem, Bernard},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}

 @misc{li2020deepergcn,
    title={DeeperGCN: All You Need to Train Deeper GCNs},
    author={Guohao Li and Chenxin Xiong and Ali Thabet and Bernard Ghanem},
    year={2020},
    eprint={2006.07739},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

 @InProceedings{li2021gnn1000,
    title={Training Graph Neural Networks with 1000 layers},
    author={Guohao Li and Matthias Müller and Bernard Ghanem and Vladlen Koltun},
    booktitle={International Conference on Machine Learning (ICML)},
    year={2021}
}