Creating Message Passing Networks
=================================

Generalizing the convolution operator to irregular domains is typically expressed as a *neighborhood aggregation* or *message passing* scheme.
With :math:`\mathbf{x}^{(k-1)}_i \in \mathbb{R}^F` denoting node features of node :math:`i` in layer :math:`(k-1)` and :math:`\mathbf{e}_{j,i} \in \mathbb{R}^D` denoting (optional) edge features from node :math:`j` to node :math:`i`, message passing graph neural networks can be described as

.. math::
  \mathbf{x}_i^{(k)} = \gamma^{(k)} \left( \mathbf{x}_i^{(k-1)}, \bigoplus_{j \in \mathcal{N}(i)} \, \phi^{(k)}\left(\mathbf{x}_i^{(k-1)}, \mathbf{x}_j^{(k-1)},\mathbf{e}_{j,i}\right) \right),

where :math:`\bigoplus` denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and :math:`\gamma` and :math:`\phi` denote differentiable functions such as MLPs (Multi Layer Perceptrons).

.. contents::
    :local:

The "MessagePassing" Base Class
-------------------------------

:pyg:`PyG` provides the :class:`~torch_geometric.nn.conv.message_passing.MessagePassing` base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation.
The user only has to define the functions :math:`\phi` , *i.e.* :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.message`, and :math:`\gamma` , *i.e.* :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.update`, as well as the aggregation scheme to use, *i.e.* :obj:`aggr="add"`, :obj:`aggr="mean"` or :obj:`aggr="max"`.

This is done with the help of the following methods:

* :obj:`MessagePassing(aggr="add", flow="source_to_target", node_dim=-2)`: Defines the aggregation scheme to use (:obj:`"add"`, :obj:`"mean"` or :obj:`"max"`) and the flow direction of message passing (either :obj:`"source_to_target"` or :obj:`"target_to_source"`).
  Furthermore, the :obj:`node_dim` attribute indicates along which axis to propagate.
* :obj:`MessagePassing.propagate(edge_index, size=None, **kwargs)`:
  The initial call to start propagating messages.
  Takes in the edge indices and all additional data which is needed to construct messages and to update node embeddings.
  Note that :func:`~torch_geometric.nn.conv.message_passing.MessagePassing.propagate` is not limited to exchanging messages in square adjacency matrices of shape :obj:`[N, N]` only, but can also exchange messages in general sparse assignment matrices, *e.g.*, bipartite graphs, of shape :obj:`[N, M]` by passing :obj:`size=(N, M)` as an additional argument.
  If set to :obj:`None`, the assignment matrix is assumed to be a square matrix.
  For bipartite graphs with two independent sets of nodes and indices, and each set holding its own information, this split can be marked by passing the information as a tuple, *e.g.* :obj:`x=(x_N, x_M)`.
* :obj:`MessagePassing.message(...)`: Constructs messages to node :math:`i` in analogy to :math:`\phi` for each edge :math:`(j,i) \in \mathcal{E}` if :obj:`flow="source_to_target"` and :math:`(i,j) \in \mathcal{E}` if :obj:`flow="target_to_source"`.
  Can take any argument which was initially passed to :meth:`propagate`.
  In addition, tensors passed to :meth:`propagate` can be mapped to the respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or :obj:`_j` to the variable name, *e.g.* :obj:`x_i` and :obj:`x_j`.
  Note that we generally refer to :math:`i` as the central nodes that aggregates information, and refer to :math:`j` as the neighboring nodes, since this is the most common notation.
* :obj:`MessagePassing.update(aggr_out, ...)`: Updates node embeddings in analogy to :math:`\gamma` for each node :math:`i \in \mathcal{V}`.
  Takes in the output of aggregation as first argument and any argument which was initially passed to :func:`~torch_geometric.nn.conv.message_passing.MessagePassing.propagate`.

Let us verify this by re-implementing two popular GNN variants, the `GCN layer from Kipf and Welling <https://arxiv.org/abs/1609.02907>`_ and the `EdgeConv layer from Wang et al. <https://arxiv.org/abs/1801.07829>`_.

Implementing the GCN Layer
--------------------------

The `GCN layer <https://arxiv.org/abs/1609.02907>`_ is mathematically defined as

.. math::

    \mathbf{x}_i^{(k)} = \sum_{j \in \mathcal{N}(i) \cup \{ i \}} \frac{1}{\sqrt{\deg(i)} \cdot \sqrt{\deg(j)}} \cdot \left( \mathbf{W}^{\top} \cdot \mathbf{x}_j^{(k-1)} \right) + \mathbf{b},

where neighboring node features are first transformed by a weight matrix :math:`\mathbf{W}`, normalized by their degree, and finally summed up.
Lastly, we apply the bias vector :math:`\mathbf{b}` to the aggregated output.
This formula can be divided into the following steps:

1. Add self-loops to the adjacency matrix.
2. Linearly transform node feature matrix.
3. Compute normalization coefficients.
4. Normalize node features in :math:`\phi`.
5. Sum up neighboring node features (:obj:`"add"` aggregation).
6. Apply a final bias vector.

Steps 1-3 are typically computed before message passing takes place.
Steps 4-5 can be easily processed using the :class:`~torch_geometric.nn.conv.message_passing.MessagePassing` base class.
The full layer implementation is shown below:

.. code-block:: python

    import torch
    from torch.nn import Linear, Parameter
    from torch_geometric.nn import MessagePassing
    from torch_geometric.utils import add_self_loops, degree

    class GCNConv(MessagePassing):
        def __init__(self, in_channels, out_channels):
            super().__init__(aggr='add')  # "Add" aggregation (Step 5).
            self.lin = Linear(in_channels, out_channels, bias=False)
            self.bias = Parameter(torch.empty(out_channels))

            self.reset_parameters()

        def reset_parameters(self):
            self.lin.reset_parameters()
            self.bias.data.zero_()

        def forward(self, x, edge_index):
            # x has shape [N, in_channels]
            # edge_index has shape [2, E]

            # Step 1: Add self-loops to the adjacency matrix.
            edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

            # Step 2: Linearly transform node feature matrix.
            x = self.lin(x)

            # Step 3: Compute normalization.
            row, col = edge_index
            deg = degree(col, x.size(0), dtype=x.dtype)
            deg_inv_sqrt = deg.pow(-0.5)
            deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
            norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

            # Step 4-5: Start propagating messages.
            out = self.propagate(edge_index, x=x, norm=norm)

            # Step 6: Apply a final bias vector.
            out = out + self.bias

            return out

        def message(self, x_j, norm):
            # x_j has shape [E, out_channels]

            # Step 4: Normalize node features.
            return norm.view(-1, 1) * x_j

:class:`~torch_geometric.nn.conv.GCNConv` inherits from :class:`~torch_geometric.nn.conv.message_passing.MessagePassing` with :obj:`"add"` propagation.
All the logic of the layer takes place in its :meth:`forward` method.
Here, we first add self-loops to our edge indices using the :meth:`torch_geometric.utils.add_self_loops` function (step 1), as well as linearly transform node features by calling the :class:`torch.nn.Linear` instance (step 2).

The normalization coefficients are derived by the node degrees :math:`\deg(i)` for each node :math:`i` which gets transformed to :math:`1/(\sqrt{\deg(i)} \cdot \sqrt{\deg(j)})` for each edge :math:`(j,i) \in \mathcal{E}`.
The result is saved in the tensor :obj:`norm` of shape :obj:`[num_edges, ]` (step 3).

We then call :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.propagate`, which internally calls :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.message`, :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.aggregate` and :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.update`.
We pass the node embeddings :obj:`x` and the normalization coefficients :obj:`norm` as additional arguments for message propagation.

In the :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.message` function, we need to normalize the neighboring node features :obj:`x_j` by :obj:`norm`.
Here, :obj:`x_j` denotes a *lifted* tensor, which contains the source node features of each edge, *i.e.*, the neighbors of each node.
Node features can be automatically lifted by appending :obj:`_i` or :obj:`_j` to the variable name.
In fact, any tensor can be converted this way, as long as they hold source or destination node features.

That is all that it takes to create a simple message passing layer.
You can use this layer as a building block for deep architectures.
Initializing and calling it is straightforward:

.. code-block:: python

    conv = GCNConv(16, 32)
    x = conv(x, edge_index)

Implementing the Edge Convolution
---------------------------------

The `edge convolutional layer <https://arxiv.org/abs/1801.07829>`_ processes graphs or point clouds and is mathematically defined as

.. math::

    \mathbf{x}_i^{(k)} = \max_{j \in \mathcal{N}(i)} h_{\mathbf{\Theta}} \left( \mathbf{x}_i^{(k-1)}, \mathbf{x}_j^{(k-1)} - \mathbf{x}_i^{(k-1)} \right),

where :math:`h_{\mathbf{\Theta}}` denotes an MLP.
In analogy to the GCN layer, we can use the :class:`~torch_geometric.nn.conv.message_passing.MessagePassing` class to implement this layer, this time using the :obj:`"max"` aggregation:

.. code-block:: python

    import torch
    from torch.nn import Sequential as Seq, Linear, ReLU
    from torch_geometric.nn import MessagePassing

    class EdgeConv(MessagePassing):
        def __init__(self, in_channels, out_channels):
            super().__init__(aggr='max') #  "Max" aggregation.
            self.mlp = Seq(Linear(2 * in_channels, out_channels),
                           ReLU(),
                           Linear(out_channels, out_channels))

        def forward(self, x, edge_index):
            # x has shape [N, in_channels]
            # edge_index has shape [2, E]

            return self.propagate(edge_index, x=x)

        def message(self, x_i, x_j):
            # x_i has shape [E, in_channels]
            # x_j has shape [E, in_channels]

            tmp = torch.cat([x_i, x_j - x_i], dim=1)  # tmp has shape [E, 2 * in_channels]
            return self.mlp(tmp)

Inside the :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.message` function, we use :obj:`self.mlp` to transform both the target node features :obj:`x_i` and the relative source node features :obj:`x_j - x_i` for each edge :math:`(j,i) \in \mathcal{E}`.

The edge convolution is actually a dynamic convolution, which recomputes the graph for each layer using nearest neighbors in the feature space.
Luckily, :pyg:`PyG` comes with a GPU accelerated batch-wise k-NN graph generation method named :meth:`torch_geometric.nn.pool.knn_graph`:

.. code-block:: python

    from torch_geometric.nn import knn_graph

    class DynamicEdgeConv(EdgeConv):
        def __init__(self, in_channels, out_channels, k=6):
            super().__init__(in_channels, out_channels)
            self.k = k

        def forward(self, x, batch=None):
            edge_index = knn_graph(x, self.k, batch, loop=False, flow=self.flow)
            return super().forward(x, edge_index)

Here, :meth:`~torch_geometric.nn.pool.knn_graph` computes a nearest neighbor graph, which is further used to call the :meth:`forward` method of :class:`~torch_geometric.nn.conv.EdgeConv`.

This leaves us with a clean interface for initializing and calling this layer:

.. code-block:: python

    conv = DynamicEdgeConv(3, 128, k=6)
    x = conv(x, batch)

Exercises
---------

Imagine we are given the following :obj:`~torch_geometric.data.Data` object:

.. code-block:: python

    import torch
    from torch_geometric.data import Data

    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())

Try to answer the following questions related to :class:`~torch_geometric.nn.conv.GCNConv`:

1. What information does :obj:`row` and :obj:`col` hold?

2. What does :meth:`~torch_geometric.utils.degree` do?

3. Why do we use :obj:`degree(col, ...)` rather than :obj:`degree(row, ...)`?

4. What does :obj:`deg_inv_sqrt[col]` and :obj:`deg_inv_sqrt[row]` do?

5. What information does :obj:`x_j` hold in the :meth:`~torch_geometric.nn.conv.MessagePassing.message` function? If :obj:`self.lin` denotes the identity function, what is the exact content of :obj:`x_j`?

6. Add an :meth:`~torch_geometric.nn.conv.MessagePassing.update` function to :class:`~torch_geometric.nn.conv.GCNConv` that adds transformed central node features to the aggregated output.

Try to answer the following questions related to :class:`~torch_geometric.nn.conv.EdgeConv`:

1. What is :obj:`x_i` and :obj:`x_j - x_i`?

2. What does :obj:`torch.cat([x_i, x_j - x_i], dim=1)` do? Why :obj:`dim = 1`?
