implement unidirected

Author: CarloLucibello

docs/Project.toml

@@ -1,7 +1,16 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
+PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"

docs/make.jl

@@ -1,5 +1,52 @@
 using Flux, NNlib, GraphNeuralNetworks, Graphs, SparseArrays
 using Documenter
+using Pluto, PlutoStaticHTML
+
+TutorialMenu = Array{Pair{String,String},1}()
+
+#
+# Generate Pluto Tutorial HTMLs
+
+# First tutorial with AD
+pluto_src_folder = joinpath(@__DIR__, "tutorials/")
+pluto_output_folder = joinpath(@__DIR__, "tutorials/")
+pluto_relative_path = "tutorials/"
+mkpath(pluto_output_folder)
+#
+#
+# Please do not use the same name as for a(n old) literate Tutorial
+pluto_files = [
+    "gnn_intro.pluto",
+    "graph_classification.pluto",
+]
+pluto_titles = [
+    "Intro to Graph Neural Networks ",
+    "Graph Classification",
+]
+
+# build menu and write files myself - tp set edit url correctly.
+for (title, file) in zip(pluto_titles, pluto_files)
+    global TutorialMenu
+    rendered = build_notebooks( #though not really parallel here
+        BuildOptions(
+            pluto_src_folder;
+            output_format=documenter_output,
+            write_files=false,
+            use_distributed=false,
+        ),
+        ["$(file).jl"],
+    )
+    write(
+        pluto_output_folder * file * ".md",
+        """
+        ```@meta
+        EditURL = "$(pluto_src_folder)$(file).jl"
+        ```
+        $(rendered[1])
+        """,
+    )
+    push!(TutorialMenu, title => joinpath(pluto_relative_path, file * ".md"))
+end
 
 DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, 
     :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux); 

src/GNNGraphs/GNNGraphs.jl

@@ -50,6 +50,7 @@ export add_nodes,
        remove_multi_edges,
        set_edge_weight,
        to_bidirected,
+       to_unidirected,
        # from Flux
        batch,
        unbatch,

src/GNNGraphs/transform.jl

@@ -235,6 +235,27 @@ function to_bidirected(g::GNNGraph{<:COO_T})
     return remove_multi_edges(g; aggr=mean)
 end
 
+"""
+    to_unidirected(g::GNNGraph)
+
+Return a graph that for each multiple edge between two nodes in `g`
+keeps only an edge in one direction.
+"""
+function to_unidirected(g::GNNGraph{<:COO_T})
+    s, t = edge_index(g)
+    w = get_edge_weight(g)
+    idxs, _ = edge_encoding(s, t, g.num_nodes, directed=false)
+    snew, tnew = edge_decoding(idxs, g.num_nodes, directed=false)
+
+    g = GNNGraph((snew, tnew, w),
+                g.num_nodes, g.num_edges, g.num_graphs,
+                g.graph_indicator,
+                g.ndata, g.edata, g.gdata)
+
+    return remove_multi_edges(g; aggr=mean)
+end
+
+
 
 """
     add_nodes(g::GNNGraph, n; [ndata])

test/GNNGraphs/transform.jl

@@ -225,16 +225,37 @@
             g2 = to_bidirected(g)
             @test g2.num_nodes == g.num_nodes
             @test g2.num_edges == 7
-            s2, t2 = edge_index(g2)         
             @test is_bidirected(g2)
             @test !has_multi_edges(g2)
-
-            s2, t2 = edge_index(g2)
+            
+            s2, t2 = edge_index(g2)         
             w2 = get_edge_weight(g2)
             @test s2 == [1, 2, 2, 3, 3, 4, 4]
             @test t2 == [2, 1, 3, 2, 4, 3, 4]
             @test w2 == [1, 1, 2, 2, 3.5, 3.5, 5]
             @test g2.edata.e == [10.0, 10.0, 20.0, 20.0, 35.0, 35.0, 50.0]
         end
     end
+
+    @testset "to_unidirected" begin
+        if GRAPH_T == :coo
+            s = [1, 2, 3, 4, 4], 
+            t = [2, 3, 4, 3, 4] 
+            w = [1.0, 2.0, 3.0, 4.0, 5.0]
+            e = [10.0, 20.0, 30.0, 40.0, 50.0]
+            g = GNNGraph(s, t, w, edata = e)
+
+            g2 = to_unidirected(g)
+            @test g2.num_nodes == g.num_nodes
+            @test g2.num_edges == 4
+            @test !has_multi_edges(g2)
+            
+            s2, t2 = edge_index(g2)         
+            w2 = get_edge_weight(g2)
+            @test s2 == [1, 2, 3, 4]
+            @test t2 == [2, 3, 4, 4]
+            @test w2 == [1, 2, 3.5, 5]
+            @test g2.edata.e == [10.0, 20.0, 35.0, 50.0]
+        end
+    end
 end