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ICLR 2021 (spotlight): Graph Convolution with Low-rank Learnable Local Filters

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# Graph Convolution with Low-rank Learnable Local Filters
<font size=4>This repo is code for the toy example 'up/down wind' in section 3 of our paper: **Graph Convolution with Low-rank Learnable Local Filters** [(paper link)](https://openreview.net/forum?id=9OHFhefeB86)

This toy example aims to show our l3gnn's expressiveness of extract discriminative features on graph to classify 'up wind' pattern against 'down wind' pattern.</font>

![up_down_wind](https://raw.githubusercontent.com/ZichenMiao/L3Net/master/up_down_wind.png)
## Requirements
pytorch==1.3.1

numpy==1.18.1

## Usage
### Create 'Up/Down Wind' dataset
```python
## generate ring graph data
python gen_data.py ring
## generate chain graph data
python gen_data.py chain
```

This will create dataset with indicated type of graph, with 5000 'Up Wind' randomly generated samples and 5000 'Down Wind' samples.

### Run experiment with 2-layer model
```python
## ChebNet with L=30, on chain graph data
python train.py 2 chain --gcn_type ChebNet --L 30
## L3-gnn with order 0,1,2, on chain graph data
python train.py 2 chain --gcn_type GCN_Bases --order_list 0 1 2
## L3-gnn with order 1, shared bases, on chain graph data
python train.py 2 chain --gcn_type GCN_Bases --order_list 1 --use_shared_bases
```
### Run experiment 1-layer model
```python
## L3-gnn with order 0,1,2, on chain graph data
python train.py 1 chain --gcn_type GCN_Bases --order_list 0 1 2
```

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