dmlc--dgl
xnouhz
5c77b61184
* [Model] MixHop for Node Classification task * [docs] update * [docs] update * [fix] remove seed option * [fix] update readme Co-authored-by: xnuohz@126.com <ubuntu@ip-172-31-44-184.us-east-2.compute.internal> Co-authored-by: Mufei Li <mufeili1996@gmail.com>
92 行
3.4 KiB
Markdown
92 行
3.4 KiB
Markdown
# DGL Implementations of MixHop
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This DGL example implements the GNN model proposed in the paper [MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing](https://arxiv.org/abs/1905.00067). For the original implementation, see [here](https://github.com/samihaija/mixhop).
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Contributor: [xnuohz](https://github.com/xnuohz)
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### Requirements
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The codebase is implemented in Python 3.6. For version requirement of packages, see below.
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```
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dgl 0.5.2
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numpy 1.19.4
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pandas 1.1.4
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tqdm 4.53.0
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torch 1.7.0
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```
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### The graph datasets used in this example
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The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
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| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
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| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
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| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
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| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
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### Usage
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###### Dataset options
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```
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--dataset str The graph dataset name. Default is 'Cora'.
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```
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###### GPU options
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```
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--gpu int GPU index. Default is -1, using CPU.
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```
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###### Model options
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```
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--epochs int Number of training epochs. Default is 2000.
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--early-stopping int Early stopping rounds. Default is 200.
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--lr float Adam optimizer learning rate. Default is 0.5.
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--lamb float L2 regularization coefficient. Default is 0.0005.
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--step-size int Period of learning rate decay. Default is 40.
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--gamma float Factor of learning rate decay. Default is 0.01.
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--hid-dim int Hidden layer dimensionalities. Default is 60.
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--num-layers int Number of GNN layers. Default is 4.
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--input-dropout float Dropout applied at input layer. Default is 0.7.
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--layer-dropout float Dropout applied at hidden layers. Default is 0.9.
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--p list List of powers of adjacency matrix. Default is [0, 1, 2].
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```
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###### Examples
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The following commands learn a neural network and predict on the test set.
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Training a MixHop model on the default dataset.
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```bash
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python main.py
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```
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Train a model for 200 epochs and perform an early stop if the validation accuracy stops getting improved for 10 epochs.
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```bash
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python main.py --epochs 200 --early-stopping 10
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```
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Train a model with a different learning rate and regularization coefficient.
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```bash
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python main.py --lr 0.001 --lamb 0.1
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```
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Train a model with different model hyperparameters.
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```bash
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python main.py --num-layers 6 --p 2 4 6
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```
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Train a model which follows the original hyperparameters on different datasets.
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```bash
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# Cora:
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python main.py --gpu 0 --dataset Cora --lr 1 --input-dropout 0.6 --lamb 5e-3 --hid-dim 100 --num-layers 3
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# Citeseer:
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python main.py --gpu 0 --dataset Citeseer --lr 0.25 --input-dropout 0.5 --lamb 5e-3 --hid-dim 60 --num-layers 3
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# Pubmed:
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python main.py --gpu 0 --dataset Pubmed --lr 0.5 --input-dropout 0.7 --lamb 5e-3 --hid-dim 60 --num-layers 3
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```
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### Performance
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| Dataset | Cora | Pubmed | Citeseer |
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| :-: | :-: | :-: | :-: |
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| Accuracy(MixHop: default architecture in Table 1) | 0.818 | 0.800 | 0.714 |
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| Accuracy(official code) | 0.610(0.156) | 0.746(0.065) | 0.700(0.017) |
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| Accuracy(DGL) | 0.801(0.005) | 0.780(0.005) | 0.692(0.005) | |