# GRAPH 2

<http://geometricdeeplearning.com/>

<https://github.com/rusty1s/pytorch_geometric>

## Data handling

A single graph = an instance of[`torch_geometric.data.Data`](https://rusty1s.github.io/pytorch_geometric/build/html/modules/data.html#torch_geometric.data.Data)

* `data.pos`: **Node position matrix** with shape`[num_nodes, num_dimensions]`
* `data.x`: **Node feature matrix** with shape`[num_nodes, num_node_features]`
* `data.edge_attr`: **Edge feature matri**x with shape`[num_edges, num_edge_features`
* `data.edge_index`: **Graph connectivity** with shape`[2, num_edges]`
* `data.y`: **Target** to train against (arbitrary shape)

### Example

```python
import torch
from torch_geometric.data import Data

edge_index = torch.tensor([[0, 1, 1, 2],
                           [1, 0, 2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)

data = Data(x=x, edge_index=edge_index)
>>> Data(x=[3, 1], edge_index=[2, 4])
```

If `edge_index` is defined as **list of index tuples**, it should be **transpose & contiguous.**

```python
edge_index = torch.tensor([[0, 1],
                           [1, 0],
                           [1, 2],
                           [2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)

data = Data(x=x, edge_index=edge_index.t().contiguous()) # Attention
>>> Data(x=[3, 1], edge_index=[2, 4])
```

## Own dataset

`torch_geometric.data.Dataset`

#### Folder

* `raw_dir` = original dataset
* `processed_dir` = processed dataset

#### Transform

* `transform` = dynamically transforms before accessing (data augmentation)
* `pre_transform` = transform before saving to disk (heavy precomputation, done once)
* `pre_filter` = manually filter out data before saving (restriction of data being of a specific class.)