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项目作者： zhulf0804

项目描述 ：
Graph Convolutional Networks for Text Classification.
高级语言： Python
项目地址: git://github.com/zhulf0804/GCN.PyTorch.git
创建时间： 2019-10-29T09:22:48Z
项目社区：https://github.com/zhulf0804/GCN.PyTorch
开源协议：
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Introduction

An inofficial PyTorch implementation of Semi-Supervised Classification with Graph Convolutional Networks.

Datasets

  • citeseer
  • cora
  • pubmed
Dataset Nodes Edges Features Classes #train #val #test
Citeseer 3,327 4,732 3,703 6 120(20 each class) 500(29, 86, 116, 106, 94, 69) 1000(77, 182, 181, 231, 169, 160)
Cora 2,708 5,429 1,433 7 140(20 each class) 500(61, 36, 78, 158, 81, 57, 29) 1000(130, 91, 144, 319, 149, 103, 64)
Pubmed 19,717 44,338 500 3 60(20 each class) 500(98, 194, 208) 1000(180, 413, 407)

Note:

GCN

W_2])

A is the adjacency matrix(adding self-loops), D is the degree matrix, X is the features, W is the parameters.

Train

  1. # usage: python train.py --dataset DATASET
  2. python train.py --dataset citeseer
  4. or
  6. python train.py --dataset cora
  8. or 
  10. python train.py --dataset pubmed

Evaluate

  1. # usage: python evaluate.py --dataset DATASET --checkpoint CHECKPOINT
  2. python evaluate.py --dataset citeseer --checkpoint checkpoints/citeseer/gcn_200.pth

Experiment results

  • Accuracy

    | | Citeseer(%) | Cora(%) | Pubmed(%) |
    | :———: | :———: | :———: | :———: |
    | this repo| 70.8 | 82.5 | 79.2 |
    | paper | 70.3 | 81.5 | 79.0 |

  • Accuracy and Loss curve(see experiments/results.png)

    • citeseer

      1.   tensorboard --logdir=experiments/citeseer

    • cora

      1.   tensorboard --logdir=experiments/cora
    • pubmed
      1.   tensorboard --logdir=experiments/pubmed

  • T-SNE Visualization

    • citeseer(see experiments/citeseer_vis.png)

      1.   python visualize.py --dataset citeseer --checkpoint checkpoints/citeseer/gcn_200.pth

    • cora(see experiments/cora_vis.png)

      1.   python visualize.py --dataset cora --checkpoint checkpoints/cora/gcn_200.pth

    • pubmed(see experiments/pubmed_vis.png)

      1.   python visualize.py --dataset pubmed --checkpoint checkpoints/pubmed/gcn_200.pth

  • Dropout and Regularization (results of citeseer for training 200 epoches)

    | weight_decay | dropout | train ac(%) | val ac(%) | test ac(%) |
    | :—-: | :—-: | :—-: | :—-: | :—-:|
    | 5e-3 | 0.7 | 90.8 | 68.4 | 70.1 |
    | 5e-3 | 0.5 | 91.7 | 70.4 | 71.5 |
    | 5e-3 | 0.2 | 93.3 | 71.0 | 71.8 |
    | 5e-3 | 0.0 | 94.2 | 68.8 | 69.6 |
    | 5e-4 | 0.7 | 97.5 | 70.0 | 70.8 |
    | 5e-4 | 0.5 | 98.3 | 70.2 | 70.8 |
    | 5e-4 | 0.2 | 98.3 | 70.8 | 70.7 |
    | 5e-4 | 0.0 | 98.3 | 71.0 | 71.7 |
    | 5e-5 | 0.7 |100.0 | 67.8 | 68.3 |
    | 5e-5 | 0.5 |100.0 | 70.6 | 69.1 |
    | 5e-5 | 0.2 |100.0 | 68.2 | 69.5 |
    | 5e-5 | 0.0 |100.0 | 67.4 | 68.0 |
    | 0.0 | 0.7 |100.0 | 68.0 | 68.0 |
    | 0.0 | 0.5 |100.0 | 68.0 | 67.6 |
    | 0.0 | 0.2 |100.0 | 63.8 | 59.7 |
    | 0.0 | 0.0 |100.0 | 64.8 | 63.6 |

    Note: Bold parameters represents the parameters in the paper.

  • All GCNLayers with regularization (results of citeseer for training 200 epoches)

    | | gcn1 | gcn2 | accuracy(%) |
    | :—-: | :—-: | :—-: | :—-: |
    | experiment| reg | reg | 71.9 |
    | paper | reg | no-reg | 70.8 |

  • Nums of train samples (results of citeseer for training 200 epoches)

    | # train samples | accuracy(%) |
    | :—-: | :—-: |
    | 120 | 70.8 |
    | 240 | 70.6 |
    | 360 | 71.5 |