【AAAI-2020 ASAPooling】ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
1.实验参数
| Parameter | Value |
|---|---|
| Batch size | 128 |
| Dataset | 可选: DD、MUTAG、NCI1、NCI109、PROTEINS, etc |
| Dropout ratio | 0.5 |
| Epochs | 10000 |
| Exp name | 自命名: DD_Glo、MUTAG_Hie, etc |
| Gpu index | 0 |
| Hid | 128 |
| Lr | 0.0005 |
| Model | 可选: ASAPooling_Global、ASAPooling_Hierarchical |
| Patience | 40 |
| Pooling ratio | 0.5 |
| Seed | 16 |
| Test batch size | 1 |
| Weight decay | 0.0001 |
2.运行程序
模型：ASAPooling_Global
数据集：DD
python main.py --exp_name=DD_Glo --dataset=DD --model=ASAPooling_Global模型：ASAPooling_Hierarchical
数据集：PROTEINS
python main.py --exp_name=PROTEINS_Hie --dataset=PROTEINS --model=ASAPooling_Hierarchical3.实验结果（8:1:1划分数据集，只做了一次实验的准确率，保留两位小数）
| DD | MUTAG | NCI1 | NCI109 | PROTEINS | |
|---|---|---|---|---|---|
| ASAPooling_Global | 61.34 | 80.00 | 64.48 | 73.91 | 73.21 |
| ASAPooling_Hierarchical | 65.55 | 70.00 | 76.89 | 73.19 | 77.68 |
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