Fix for quadratic batching in #99

src/GNNGraphs/transform.jl

@@ -311,6 +311,7 @@ function SparseArrays.blockdiag(g1::GNNGraph, gothers::GNNGraph...)
     end
     return g
 end
+SparseArrays.blockdiag(gs::Vector{GNNGraph}) = SparseArrays.blockdiag(gs...) 
 
 """
     batch(gs::Vector{<:GNNGraph})
@@ -358,8 +359,33 @@ julia> g12.ndata.x
  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 ```
 """
-Flux.batch(gs::Vector{<:GNNGraph}) = blockdiag(gs...)
-
+function Flux.batch(gs::Vector{<:GNNGraph})
+    nodes = [g.num_nodes for g in gs]
+
+    if all(y -> isa(y, COO_T), [g.graph for g in gs] )
+        edge_indices = [edge_index(g) for g in gs]
+        nodesum = cumsum([0, nodes...])[1:end-1]
+        s = reduce(vcat, [ei[1] .+ nodesum[ii] for (ii,ei) in enumerate(edge_indices)])
+        t = reduce(vcat, [ei[2] .+ nodesum[ii] for (ii,ei) in enumerate(edge_indices)])
+        w = reduce(vcat, [get_edge_weight(g) for g in gs])
+        w = w isa Vector{Nothing} ? nothing : w 
+        graph = (s, t, w)
+        graph_indicator = vcat([ones_like(ei[1],Int,nodes[ii]) .+ (ii - 1) for (ii,ei) in enumerate(edge_indices)]...)
+    elseif all(y -> isa(y, ADJMAT_T), [g.graph for g in gs] )
+        graph = blockdiag([g.graph for g in gs]...)
+        graph_indicator = vcat([ones_like(graph,Int,nodes[ii]) .+ (ii - 1) for ii in 1:length(nodes)]...)
+    end
+
+    GNNGraph(graph,
+        sum(nodes), 
+        sum([g.num_edges for g in gs]),
+        sum([g.num_graphs for g in gs]),
+        graph_indicator,
+        cat_features([g.ndata for g in gs]),
+        cat_features([g.edata for g in gs]),
+        cat_features([g.gdata for g in gs]),
+    )
+end
 
 """
     unbatch(g::GNNGraph)

src/GNNGraphs/utils.jl

@@ -27,6 +27,20 @@ function cat_features(x1::NamedTuple, x2::NamedTuple)
     NamedTuple(k => cat_features(getfield(x1,k), getfield(x2,k)) for k in keys(x1))
 end
 
+function cat_features(xs::Vector{NamedTuple{T1, T2}})  where {T1, T2}
+    symbols = [sort(collect(keys(x))) for x in xs]
+    all(y->y==symbols[1], symbols) || @error "cannot concatenate feature data with different keys"
+    length(xs) == 1 && return xs[1] 
+
+    # concatenate 
+    syms = symbols[1]
+    dims = [max(1, ndims(xs[1][k])) for k in syms] # promote scalar to 1D
+    methods = [dim == 1 ? vcat : hcat for dim in dims] # use optimized reduce(hcat,xs) or reduce(vcat,xs)
+    NamedTuple(
+        k => reduce(methods[ii],[x[k] for x in xs]) for (ii,k) in enumerate(syms)
+    )
+end
+
 # Turns generic type into named tuple
 normalize_graphdata(data::Nothing; kws...) = NamedTuple()
 

test/GNNGraphs/transform.jl

@@ -25,6 +25,7 @@
 
         g12 = Flux.batch([g1, g2])
         g12b = blockdiag(g1, g2)
+        @test g12 == g12b
 
         g123 = Flux.batch([g1, g2, g3])
         @test g123.graph_indicator == [fill(1, 10); fill(2, 4); fill(3, 7)]