add AGNNConv

docs/src/api/messagepassing.md

@@ -21,5 +21,7 @@ propagate
 ## Built-in message functions
 
 ```@docs
-copyxj
+copy_xi
+copy_xj
+xi_dot_xj
 ```

docs/src/messagepassing.md

@@ -88,4 +88,4 @@ See the [`GATConv`](@ref) implementation [here](https://github.com/CarloLucibell
 ## Built-in message functions
 
 In order to exploit optimized specializations of the [`propagate`](@ref), it is recommended 
-to use built-in message functions such as [`copyxj`](@ref) whenever possible. 
+to use built-in message functions such as [`copy_xj`](@ref) whenever possible. 

src/GraphNeuralNetworks.jl

@@ -28,14 +28,15 @@ export
 
     # msgpass
     apply_edges, propagate,
-    copyxj,
+    copy_xj, copy_xi, xi_dot_xj,
 
     # layers/basic
     GNNLayer,
     GNNChain,
     WithGraph,
 
     # layers/conv
+    AGNNConv,
     CGConv,
     ChebConv,
     EdgeConv,

src/deprecations.jl

@@ -1,10 +1,5 @@
-# Deprecated in v0.1 
+## Deprecated in v0.2
 
-@deprecate GINConv(nn; eps=0, aggr=+)  GINConv(nn, eps; aggr)
-
-
-# Deprecated in v0.2
-# TODO check if argument order is exact
 function compute_message end
 function update_node end
 function update_edge end
@@ -29,3 +24,7 @@ function propagate(l::GNNLayer, g::GNNGraph, aggr, x, e=nothing)
     e = update_edge(l, e, m)
     return x, e
 end
+
+## Deprecated in v0.3 
+
+@deprecate copyxj(xi, xj, e)  copy_xj(xi, xj, e) 

src/layers/conv.jl

@@ -50,7 +50,7 @@ function (l::GCNConv)(g::GNNGraph, x::AbstractMatrix{T}) where T
     # @assert all(>(0), degree(g, T, dir=:in))
     c = 1 ./ sqrt.(degree(g, T, dir=:in))
     x = x .* c'
-    x = propagate(copyxj, g, +, xj=x)
+    x = propagate(copy_xj, g, +, xj=x)
     x = x .* c'
     if Dout >= Din
         x = l.weight * x
@@ -179,7 +179,7 @@ end
 
 function (l::GraphConv)(g::GNNGraph, x::AbstractMatrix)
     check_num_nodes(g, x)
-    m = propagate(copyxj, g, l.aggr, xj=x)
+    m = propagate(copy_xj, g, l.aggr, xj=x)
     x = l.σ.(l.weight1 * x .+ l.weight2 * m .+ l.bias)
     return x
 end
@@ -206,7 +206,7 @@ Graph attentional layer from the paper [Graph Attention Networks](https://arxiv.
 
 Implements the operation
 ```math
-\mathbf{x}_i' = \sum_{j \in N(i)} \alpha_{ij} W \mathbf{x}_j
+\mathbf{x}_i' = \sum_{j \in N(i) \cup \{i\}} \alpha_{ij} W \mathbf{x}_j
 ```
 where the attention coefficients ``\alpha_{ij}`` are given by
 ```math
@@ -338,7 +338,7 @@ function (l::GatedGraphConv)(g::GNNGraph, H::AbstractMatrix{S}) where {S<:Real}
     end
     for i = 1:l.num_layers
         M = view(l.weight, :, :, i) * H
-        M = propagate(copyxj, g, l.aggr; xj=M)
+        M = propagate(copy_xj, g, l.aggr; xj=M)
         H, _ = l.gru(H, M)
     end
     H
@@ -420,7 +420,7 @@ GINConv(nn, ϵ; aggr=+) = GINConv(nn, ϵ, aggr)
 
 function (l::GINConv)(g::GNNGraph, x::AbstractMatrix)
     check_num_nodes(g, x)
-    m = propagate(copyxj, g, l.aggr, xj=x)
+    m = propagate(copy_xj, g, l.aggr, xj=x)
     l.nn((1 + ofeltype(x, l.ϵ)) * x + m)
 end
 
@@ -542,7 +542,7 @@ end
 
 function (l::SAGEConv)(g::GNNGraph, x::AbstractMatrix)
     check_num_nodes(g, x)
-    m = propagate(copyxj, g, l.aggr, xj=x)
+    m = propagate(copy_xj, g, l.aggr, xj=x)
     x = l.σ.(l.weight * vcat(x, m) .+ l.bias)
     return x 
 end
@@ -711,3 +711,56 @@ function Base.show(io::IO, l::CGConv)
     print(io, ", residual=$(l.residual)")
     print(io, ")")
 end
+
+
+@doc raw"""
+    AGNNConv(init_beta=1f0)
+
+Attention-based Graph Neural Network layer from paper [Attention-based
+Graph Neural Network for Semi-Supervised Learning](https://arxiv.org/abs/1803.03735).
+
+THe forward pass is given by
+```math
+\mathbf{x}_i' = \sum_{j \in {N(i) \cup \{i\}} \alpha_{ij} W \mathbf{x}_j
+```
+where the attention coefficients ``\alpha_{ij}`` are given by
+```math
+\alpha_{ij} =\frac{e^{\beta \cos(\mathbf{x}_i, \mathbf{x}_j)}}
+                  {\sum_{j'}e^{\beta \cos(\mathbf{x}_i, \mathbf{x}_j'}}
+```
+with the cosine distance defined by
+```math 
+\cos(\mathbf{x}_i, \mathbf{x}_j) = 
+  \mathbf{x}_i \cdot \mathbf{x}_j / \lVert\mathbf{x}_i\rVert \lVert\mathbf{x}_j\rVert``
+```
+and ``\beta`` a trainable parameter.
+
+# Arguments
+
+- `init_beta`: The initial value of ``\beta``.
+"""
+struct AGNNConv{A<:AbstractVector} <: GNNLayer
+    β::A
+end
+
+@functor AGNNConv
+
+function AGNNConv(init_beta = 1f0)
+    AGNNConv([init_beta])
+end
+
+function (l::AGNNConv)(g::GNNGraph, x::AbstractMatrix)
+    check_num_nodes(g, x)
+    g = add_self_loops(g)
+
+    xn = x ./ sqrt.(sum(x.^2, dims=1))
+    cos_dist = apply_edges(xi_dot_xj, g, xi=xn, xj=xn)
+    α = softmax_edge_neighbors(g, l.β .* cos_dist)
+
+    x = propagate(g, +; xj=x, e=α) do xi, xj, α
+            α .* xj
+        end
+
+    return x  
+end
+

src/msgpass.jl

@@ -139,26 +139,32 @@ _scatter(aggr, m::AbstractArray, t) = NNlib.scatter(aggr, m, t)
 
 ### SPECIALIZATIONS OF PROPAGATE ###
 """
-    copyxj(xi, xj, e) = xj
+    copy_xj(xi, xj, e) = xj
 """
-copyxj(xi, xj, e) = xj
+copy_xj(xi, xj, e) = xj
 
-# copyxi(xi, xj, e) = xi
-# ximulxj(xi, xj, e) = xi .* xj
-# xiaddxj(xi, xj, e) = xi .+ xj
+"""
+    copy_xi(xi, xj, e) = xi
+"""
+copy_xi(xi, xj, e) = xi
+
+"""
+    xi_dot_xj(xi, xj, e) = sum(xi .* xj, dims=1)
+"""
+xi_dot_xj(xi, xj, e) = sum(xi .* xj, dims=1)
 
 
-function propagate(::typeof(copyxj), g::GNNGraph, ::typeof(+), xi, xj::AbstractMatrix, e)
+function propagate(::typeof(copy_xj), g::GNNGraph, ::typeof(+), xi, xj::AbstractMatrix, e)
     A = adjacency_matrix(g)
     return xj * A
 end
 
 ## avoid the fast path on gpu until we have better cuda support
-function propagate(::typeof(copyxj), g::GNNGraph{<:Union{COO_T,SPARSE_T}}, ::typeof(+), xi, xj::AnyCuMatrix, e)
-    propagate((xi,xj,e)->copyxj(xi,xj,e), g, +, xi, xj, e)
+function propagate(::typeof(copy_xj), g::GNNGraph{<:Union{COO_T,SPARSE_T}}, ::typeof(+), xi, xj::AnyCuMatrix, e)
+    propagate((xi,xj,e)->copy_xj(xi,xj,e), g, +, xi, xj, e)
 end
 
-# function propagate(::typeof(copyxj), g::GNNGraph, ::typeof(mean), xi, xj::AbstractMatrix, e)
+# function propagate(::typeof(copy_xj), g::GNNGraph, ::typeof(mean), xi, xj::AbstractMatrix, e)
 #     A = adjacency_matrix(g)
 #     D = compute_degree(A)
 #     return xj * A * D

test/deprecations.jl

@@ -23,7 +23,7 @@
         end
 
         function new_forward(l, g, x)
-            x = propagate(copyxj, g, +, xj=x) 
+            x = propagate(copy_xj, g, +, xj=x) 
             return l.σ.(l.weight * x .+ l.bias)
         end
 

test/layers/conv.jl

@@ -158,4 +158,13 @@
             test_layer(l, g, rtol=1e-5, outsize=(out_channel, g.num_nodes)) 
         end
     end
+
+
+    @testset "AGNNConv" begin
+        l = AGNNConv()
+        l.β == [1f0]
+        for g in test_graphs
+            test_layer(l, g, rtol=1e-5, outsize=(in_channel, g.num_nodes)) 
+        end
+    end
 end