# Message Passing

## Index

`GraphNeuralNetworks.aggregate_neighbors`

`GraphNeuralNetworks.apply_edges`

`GraphNeuralNetworks.copy_xi`

`GraphNeuralNetworks.copy_xj`

`GraphNeuralNetworks.e_mul_xj`

`GraphNeuralNetworks.propagate`

`GraphNeuralNetworks.w_mul_xj`

`GraphNeuralNetworks.xi_dot_xj`

`GraphNeuralNetworks.xi_sub_xj`

`GraphNeuralNetworks.xj_sub_xi`

## Interface

`GraphNeuralNetworks.apply_edges`

— Function```
apply_edges(fmsg, g, [layer]; [xi, xj, e])
apply_edges(fmsg, g, [layer,] xi, xj, e=nothing)
```

Returns the message from node `j`

to node `i`

applying the message function `fmsg`

on the edges in graph `g`

. In the message-passing scheme, the incoming messages from the neighborhood of `i`

will later be aggregated in order to update the features of node `i`

(see `aggregate_neighbors`

).

The function `fmsg`

operates on batches of edges, therefore `xi`

, `xj`

, and `e`

are tensors whose last dimension is the batch size, or can be named tuples of such tensors.

If also a `GNNLayer`

`layer`

is provided, it will be passed to `fmsg`

as a first argument.

**Arguments**

`g`

: An`AbstractGNNGraph`

.`xi`

: An array or a named tuple containing arrays whose last dimension's size is`g.num_nodes`

. It will be appropriately materialized on the target node of each edge (see also`edge_index`

).`xj`

: As`xi`

, but now to be materialized on each edge's source node.`e`

: An array or a named tuple containing arrays whose last dimension's size is`g.num_edges`

.`fmsg`

: A function that takes as inputs the edge-materialized`xi`

,`xj`

, and`e`

. These are arrays (or named tuples of arrays) whose last dimension' size is the size of a batch of edges. The output of`f`

has to be an array (or a named tuple of arrays) with the same batch size. If also`layer`

is passed to propagate, the signature of`fmsg`

has to be`fmsg(layer, xi, xj, e)`

instead of`fmsg(xi, xj, e)`

.`layer`

: A`GNNLayer`

. If provided it will be passed to`fmsg`

as a first argument.

See also `propagate`

and `aggregate_neighbors`

.

`GraphNeuralNetworks.aggregate_neighbors`

— Function`aggregate_neighbors(g, aggr, m)`

Given a graph `g`

, edge features `m`

, and an aggregation operator `aggr`

(e.g `+, min, max, mean`

), returns the new node features

\[\mathbf{x}_i = \square_{j \in \mathcal{N}(i)} \mathbf{m}_{j\to i}\]

Neighborhood aggregation is the second step of `propagate`

, where it comes after `apply_edges`

.

`GraphNeuralNetworks.propagate`

— Function```
propagate(fmsg, g, aggr [layer]; [xi, xj, e])
propagate(fmsg, g, aggr, [layer,] xi, xj, e=nothing)
```

Performs message passing on graph `g`

. Takes care of materializing the node features on each edge, applying the message function `fmsg`

, and returning an aggregated message $\bar{\mathbf{m}}$ (depending on the return value of `fmsg`

, an array or a named tuple of arrays with last dimension's size `g.num_nodes`

).

If also a `GNNLayer`

`layer`

is provided, it will be passed to `fmsg`

as a first argument.

It can be decomposed in two steps:

```
m = apply_edges(fmsg, g, xi, xj, e)
m̄ = aggregate_neighbors(g, aggr, m)
```

GNN layers typically call `propagate`

in their forward pass, providing as input `f`

a closure.

**Arguments**

`g`

: A`GNNGraph`

.`xi`

: An array or a named tuple containing arrays whose last dimension's size is`g.num_nodes`

. It will be appropriately materialized on the target node of each edge (see also`edge_index`

).`xj`

: As`xj`

, but to be materialized on edges' sources.`e`

: An array or a named tuple containing arrays whose last dimension's size is`g.num_edges`

.`fmsg`

: A generic function that will be passed over to`apply_edges`

. Has to take as inputs the edge-materialized`xi`

,`xj`

, and`e`

(arrays or named tuples of arrays whose last dimension' size is the size of a batch of edges). Its output has to be an array or a named tuple of arrays with the same batch size. If also`layer`

is passed to propagate, the signature of`fmsg`

has to be`fmsg(layer, xi, xj, e)`

instead of`fmsg(xi, xj, e)`

.`layer`

: A`GNNLayer`

. If provided it will be passed to`fmsg`

as a first argument.`aggr`

: Neighborhood aggregation operator. Use`+`

,`mean`

,`max`

, or`min`

.

**Examples**

```
using GraphNeuralNetworks, Flux
struct GNNConv <: GNNLayer
W
b
σ
end
Flux.@functor GNNConv
function GNNConv(ch::Pair{Int,Int}, σ=identity)
in, out = ch
W = Flux.glorot_uniform(out, in)
b = zeros(Float32, out)
GNNConv(W, b, σ)
end
function (l::GNNConv)(g::GNNGraph, x::AbstractMatrix)
message(xi, xj, e) = l.W * xj
m̄ = propagate(message, g, +, xj=x)
return l.σ.(m̄ .+ l.bias)
end
l = GNNConv(10 => 20)
l(g, x)
```

See also `apply_edges`

and `aggregate_neighbors`

.

## Built-in message functions

`GraphNeuralNetworks.copy_xi`

— Function`copy_xi(xi, xj, e) = xi`

`GraphNeuralNetworks.copy_xj`

— Function`copy_xj(xi, xj, e) = xj`

`GraphNeuralNetworks.xi_dot_xj`

— Function`xi_dot_xj(xi, xj, e) = sum(xi .* xj, dims=1)`

`GraphNeuralNetworks.xi_sub_xj`

— Function`xi_sub_xj(xi, xj, e) = xi .- xj`

`GraphNeuralNetworks.xj_sub_xi`

— Function`xj_sub_xi(xi, xj, e) = xj .- xi`

`GraphNeuralNetworks.e_mul_xj`

— Function`e_mul_xj(xi, xj, e) = reshape(e, (...)) .* xj`

Reshape `e`

into broadcast compatible shape with `xj`

(by prepending singleton dimensions) then perform broadcasted multiplication.

`GraphNeuralNetworks.w_mul_xj`

— Function`w_mul_xj(xi, xj, w) = reshape(w, (...)) .* xj`

Similar to `e_mul_xj`

but specialized on scalar edge features (weights).