implement unbatch and add_nodes (#65)

* add unbatch

* implement add_nodes

* cleanup

docs/src/gnngraph.md

@@ -20,7 +20,7 @@ lg = erdos_renyi(10, 30)
 g = GNNGraph(lg)
 
 # Same as above using convenience method rand_graph
-g = rand_graph(10, 30)
+g = rand_graph(10, 60)
 
 # From an adjacency matrix
 A = sprand(10, 10, 0.3)

src/GNNGraphs/GNNGraphs.jl

@@ -21,9 +21,9 @@ export GNNGraph, node_features, edge_features, graph_features
 include("query.jl")
 export  edge_index, adjacency_list, normalized_laplacian, scaled_laplacian,
         graph_indicator
-    
+
 include("transform.jl")
-export add_edges, add_self_loops, remove_self_loops, getgraph
+export add_nodes, add_edges, add_self_loops, remove_self_loops, getgraph
 
 include("generate.jl")
 export rand_graph
@@ -38,6 +38,6 @@ export
     # from SparseArrays
     sprand, sparse, blockdiag,
     # from Flux
-    batch
+    batch, unbatch
 
 end #module

src/GNNGraphs/generate.jl

@@ -1,14 +1,52 @@
 """
-    rand_graph(n, m; directed=false, kws...)
+    rand_graph(n, m; bidirected=true, kws...)
 
-Generate a random (Erdós-Renyi) `GNNGraph` with `n` nodes.
+Generate a random (Erdós-Renyi) `GNNGraph` with `n` nodes
+and `m` edges.
 
-If `directed=false` the output will contain `2m` edges:
-the reverse edge of each edge will be present.
-If `directed=true` instead, `m` unrelated edges are generated.
+If `bidirected=true` the reverse edge of each edge will be present.
+If `bidirected=false` instead, `m` unrelated edges are generated.
+In any case, the output graph will contain no self-loops or multi-edges.
 
-Additional keyword argument  will be fed to the [`GNNGraph`](@ref) constructor.
+Additional keyword arguments will be passed to the [`GNNGraph`](@ref) constructor.
+
+# Usage
+
+```juliarepl
+julia> g = rand_graph(5, 4, bidirected=false)
+GNNGraph:
+    num_nodes = 5
+    num_edges = 4
+    num_graphs = 1
+    ndata:
+    edata:
+    gdata:
+
+
+julia> edge_index(g)
+([1, 3, 3, 4], [5, 4, 5, 2])
+
+# In the bidirected case, edge data will be duplicated on the reverse edges if needed.
+julia> g = rand_graph(5, 4, edata=rand(16, 2))
+GNNGraph:
+    num_nodes = 5
+    num_edges = 4
+    num_graphs = 1
+    ndata:
+    edata:
+        e => (16, 4)
+    gdata:
+
+# Each edge has a reverse
+julia> edge_index(g)
+([1, 3, 3, 4], [3, 4, 1, 3])
+
+```
 """
-function rand_graph(n::Integer, m::Integer; directed=false, kws...)
-    return GNNGraph(Graphs.erdos_renyi(n, m, is_directed=directed); kws...)    
+function rand_graph(n::Integer, m::Integer; bidirected=true, kws...)
+    if bidirected
+        @assert iseven(m) "Need even number of edges for bidirected graphs, given m=$m."
+    end
+    m2 = bidirected ? m÷2 : m
+    return GNNGraph(Graphs.erdos_renyi(n, m2, is_directed=!bidirected); kws...)    
 end

src/GNNGraphs/gnngraph.jl

@@ -56,7 +56,7 @@ functionality from that library.
               Optionally, also edge weights can be given: `(source, target, weights)`.
     - `:sparse`. A sparse adjacency matrix representation.
     - `:dense`. A dense adjacency matrix representation.  
-    Default `:coo`.
+    Defaults to `:coo`, currently the most supported type.
 - `dir`: The assumed edge direction when given adjacency matrix or adjacency list input data `g`. 
         Possible values are `:out` and `:in`. Default `:out`.
 - `num_nodes`: The number of nodes. If not specified, inferred from `g`. Default `nothing`.

src/GNNGraphs/query.jl

@@ -62,7 +62,7 @@ Graphs.is_directed(::Type{<:GNNGraph}) = true
 Return the adjacency list representation (a vector of vectors)
 of the graph `g`.
 
-Calling `a` the adjacency list, if `dir=:out`
+Calling `a` the adjacency list, if `dir=:out` than
 `a[i]` will contain the neighbors of node `i` through
 outgoing edges. If `dir=:in`, it will contain neighbors from
 incoming edges instead.
@@ -75,7 +75,7 @@ end
 
 function Graphs.adjacency_matrix(g::GNNGraph{<:COO_T}, T::DataType=Int; dir=:out)
     if g.graph[1] isa CuVector
-        # TODO revisi after https://github.com/JuliaGPU/CUDA.jl/pull/1152
+        # TODO revisit after https://github.com/JuliaGPU/CUDA.jl/pull/1152
         A, n, m = to_dense(g.graph, T, num_nodes=g.num_nodes)
     else
         A, n, m = to_sparse(g.graph, T, num_nodes=g.num_nodes)

src/GNNGraphs/transform.jl

@@ -54,8 +54,7 @@ end
 """
     add_edges(g::GNNGraph, s::AbstractVector, t::AbstractVector; [edata])
 
-Add to graph `g` the edges with source nodes `s` and target nodes `t`.    
-
+Add to graph `g` the edges with source nodes `s` and target nodes `t`.
 """
 function add_edges(g::GNNGraph{<:COO_T}, 
         snew::AbstractVector{<:Integer}, 
@@ -79,6 +78,25 @@ function add_edges(g::GNNGraph{<:COO_T},
             g.ndata, edata, g.gdata)
 end
 
+
+"""
+    add_nodes(g::GNNGraph, n; [ndata])
+
+Add `n` new nodes to graph `g`. In the 
+new graph, these nodes will have indexes from `g.num_nodes + 1`
+to `g.num_nodes + n`.
+"""
+function add_nodes(g::GNNGraph{<:COO_T}, n::Integer; ndata=(;))
+    ndata = normalize_graphdata(ndata, default_name=:x, n=n)
+    ndata = cat_features(g.ndata, ndata)
+
+    GNNGraph(g.graph, 
+            g.num_nodes + n, g.num_edges, g.num_graphs, 
+            g.graph_indicator,
+            ndata, g.edata, g.gdata)
+end
+
+
 function SparseArrays.blockdiag(g1::GNNGraph, g2::GNNGraph)
     nv1, nv2 = g1.num_nodes, g2.num_nodes
     if g1.graph isa COO_T
@@ -117,8 +135,6 @@ function SparseArrays.blockdiag(A1::AbstractMatrix, A2::AbstractMatrix)
             O2 A2]
 end
 
-### Cat public interfaces #############
-
 """
     blockdiag(xs::GNNGraph...)
 
@@ -133,14 +149,115 @@ function SparseArrays.blockdiag(g1::GNNGraph, gothers::GNNGraph...)
 end
 
 """
-    batch(xs::Vector{<:GNNGraph})
+    batch(gs::Vector{<:GNNGraph})
 
 Batch together multiple `GNNGraph`s into a single one 
 containing the total number of original nodes and edges.
 
 Equivalent to [`SparseArrays.blockdiag`](@ref).
+See also [`Flux.unbatch`](@ref).
+
+# Usage
+
+```juliarepl
+julia> g1 = rand_graph(4, 6, ndata=ones(8, 4))
+GNNGraph:
+    num_nodes = 4
+    num_edges = 6
+    num_graphs = 1
+    ndata:
+        x => (8, 4)
+    edata:
+    gdata:
+
+
+julia> g2 = rand_graph(7, 4, ndata=zeros(8, 7))
+GNNGraph:
+    num_nodes = 7
+    num_edges = 4
+    num_graphs = 1
+    ndata:
+        x => (8, 7)
+    edata:
+    gdata:
+
+
+julia> g12 = Flux.batch([g1, g2])
+GNNGraph:
+    num_nodes = 11
+    num_edges = 10
+    num_graphs = 2
+    ndata:
+        x => (8, 11)
+    edata:
+    gdata:
+
+
+julia> g12.ndata.x
+8×11 Matrix{Float64}:
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
+ 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...)
+
+
 """
-Flux.batch(xs::Vector{<:GNNGraph}) = blockdiag(xs...)
+    unbatch(g::GNNGraph)
+
+Opposite of the [`Flux.batch`](@ref) operation, returns 
+an array of the individual graphs batched together in `g`.
+
+See also [`Flux.batch`](@ref) and [`getgraph`](@ref).
+
+# Usage
+
+```juliarepl
+julia> gbatched = Flux.batch([rand_graph(5, 6), rand_graph(10, 8), rand_graph(4,2)])
+GNNGraph:
+    num_nodes = 19
+    num_edges = 16
+    num_graphs = 3
+    ndata:
+    edata:
+    gdata:
+
+julia> Flux.unbatch(gbatched)
+3-element Vector{GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}}:
+ GNNGraph:
+    num_nodes = 5
+    num_edges = 6
+    num_graphs = 1
+    ndata:
+    edata:
+    gdata:
+
+ GNNGraph:
+    num_nodes = 10
+    num_edges = 8
+    num_graphs = 1
+    ndata:
+    edata:
+    gdata:
+
+ GNNGraph:
+    num_nodes = 4
+    num_edges = 2
+    num_graphs = 1
+    ndata:
+    edata:
+    gdata:
+```
+"""
+function Flux.unbatch(g::GNNGraph) 
+    [getgraph(g, i) for i in 1:g.num_graphs]
+end
 
 
 """

test/GNNGraphs/generate.jl

@@ -1,20 +1,21 @@
 @testset "generate" begin 
     @testset "rand_graph" begin
         n, m = 10, 20
+        m2 = m ÷ 2
         x = rand(3, n)
-        e = rand(4, m)
+        e = rand(4, m2)
         g = rand_graph(n, m, ndata=x, edata=e, graph_type=GRAPH_T)
         @test g.num_nodes == n
-        @test g.num_edges == 2m
+        @test g.num_edges == m
         @test g.ndata.x === x 
         if GRAPH_T == :coo
             s, t = edge_index(g)
-            @test s[1:m] == t[m+1:end]
-            @test t[1:m] == s[m+1:end]
-            @test g.edata.e[:,1:m] == e
-            @test g.edata.e[:,m+1:end] == e
+            @test s[1:m2] == t[m2+1:end]
+            @test t[1:m2] == s[m2+1:end]
+            @test g.edata.e[:,1:m2] == e
+            @test g.edata.e[:,m2+1:end] == e
         end
-        g = rand_graph(n, m, directed=true, graph_type=GRAPH_T)
+        g = rand_graph(n, m, bidirected=false, graph_type=GRAPH_T)
         @test g.num_nodes == n
         @test g.num_edges == m
     end

test/GNNGraphs/transform.jl

@@ -42,6 +42,20 @@
         @test g123.gdata.u == [g1.gdata.u, g2.gdata.u, g3.gdata.u]
     end
 
+    @testset "unbatch" begin
+        g1 = rand_graph(10, 20)
+        g2 = rand_graph(5, 10)
+        g12 = Flux.batch([g1, g2])
+        gs = Flux.unbatch([g1,g2])
+        @test length(gs) == 2
+        @test gs[1].num_nodes == 10
+        @test gs[1].num_edges == 20
+        @test gs[1].num_graphs == 1
+        @test gs[2].num_nodes == 5
+        @test gs[2].num_edges == 10
+        @test gs[2].num_graphs == 1
+    end
+
     @testset "getgraph"  begin
         g1 = GNNGraph(random_regular_graph(10,2), ndata=rand(16,10), graph_type=GRAPH_T)
         g2 = GNNGraph(random_regular_graph(4,2), ndata=rand(16,4), graph_type=GRAPH_T)
@@ -80,4 +94,15 @@
             @test all(gnew.edata.e2[:,5] .== 0)           
         end
     end
+
+    @testset "add_nodes" begin
+        if GRAPH_T == :coo
+            g = rand_graph(6, 4, ndata=rand(2, 6), graph_type=GRAPH_T)
+            gnew = add_nodes(g, 5, ndata=ones(2, 5))
+            @test gnew.num_nodes == g.num_nodes + 5
+            @test gnew.num_edges == g.num_edges
+            @test gnew.num_graphs == g.num_graphs
+            @test all(gnew.ndata.x[:,7:11] .== 1)         
+        end
+    end
 end