remove GraphLaplacians

Project.toml

@@ -9,9 +9,7 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-GraphLaplacians = "a1251efa-393a-423f-9d7b-faaecba535dc"
 GraphMLDatasets = "21828b05-d3b3-40ad-870e-a4bc2f52d5e8"
-GraphSignals = "3ebe565e-a4b5-49c6-aed2-300248c3a9c1"
 LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
@@ -26,9 +24,7 @@ CUDA = "3.3"
 DataStructures = "0.18"
 FillArrays = "0.11, 0.12"
 Flux = "0.12"
-GraphLaplacians = "0.1"
 GraphMLDatasets = "0.1"
-GraphSignals = "0.2"
 LightGraphs = "1.3"
 NNlib = "0.7"
 NNlibCUDA = "0.1"

src/GeometricFlux.jl

@@ -1,18 +1,19 @@
 module GeometricFlux
 
-using Base: check_channel_state
 using Statistics: mean
-using LinearAlgebra: Adjoint, norm, Transpose
+using LinearAlgebra
 using FillArrays: Fill
 
 using CUDA
 using Flux
 using Flux: glorot_uniform, leakyrelu, GRUCell, @functor
 using NNlib, NNlibCUDA
 using Zygote
+using ChainRulesCore
 
-import GraphLaplacians
-using GraphLaplacians: normalized_laplacian, scaled_laplacian
+
+# import GraphLaplacians
+# using GraphLaplacians: normalized_laplacian, scaled_laplacian
 # using GraphLaplacians: adjacency_matrix
 # using Reexport
 # @reexport using GraphSignals
@@ -23,7 +24,7 @@ export
     # featured_graph
     FeaturedGraph,
     adjacency_list,
-    graph,
+    # graph,
     # has_graph,
     node_feature, edge_feature, global_feature,
     # ne, nv, adjacency_matrix, # from LightGraphs

src/featured_graph.jl

@@ -171,9 +171,8 @@ function LightGraphs.adjacency_matrix(fg::FeaturedGraph, T::DataType=Int; dir=:o
     return dir == :out ? adj_mat : adj_mat'
 end
 
-Zygote.@nograd adjacency_matrix, adjacency_list #, FeaturedGraph
+Zygote.@nograd adjacency_matrix, adjacency_list
 
-# using ChainRulesCore
 
 # function ChainRulesCore.rrule(::typeof(copy), x)
 #     copy_pullback(ȳ) = (NoTangent(), ȳ)
@@ -233,18 +232,71 @@ end
 
 LightGraphs.is_directed(g::AbstractMatrix) = !issymmetric(Matrix(g))
 
-function LightGraphs.laplacian_matrix(fg::FeaturedGraph, T::DataType=Int; dir::Symbol=:unspec)
-    if dir == :unspec
-        dir = is_directed(g) ? :both : :out
+# function LightGraphs.laplacian_matrix(fg::FeaturedGraph, T::DataType=Int; dir::Symbol=:out)
+#     A = adjacency_matrix(fg, T; dir=dir)
+#     D = Diagonal(vec(sum(A; dims=2)))
+#     return D - A
+# end
+
+## from GraphLaplacians
+
+"""
+    normalized_laplacian(g[, T]; selfloop=false, dir=:out)
+
+Normalized Laplacian matrix of graph `g`.
+
+# Arguments
+
+- `g`: should be a adjacency matrix, `FeaturedGraph`, `SimpleGraph`, `SimpleDiGraph` (from LightGraphs) or `SimpleWeightedGraph`, `SimpleWeightedDiGraph` (from SimpleWeightedGraphs).
+- `T`: result element type of degree vector; default is the element type of `g` (optional).
+- `selfloop`: adding self loop while calculating the matrix (optional).
+- `dir`: the edge directionality considered (:out, :in, :both).
+"""
+function normalized_laplacian(fg::FeaturedGraph, T::DataType=Int; selfloop::Bool=false, dir::Symbol=:out)
+    A = adjacency_matrix(fg, T; dir=dir)
+    selfloop && (A += I)
+    degs = vec(sum(A; dims=2))
+    inv_sqrtD = Diagonal(inv.(sqrt.(degs)))
+    return I - inv_sqrtD * A * inv_sqrtD
+end
+
+@doc raw"""
+    scaled_laplacian(g[, T]; dir=:out)
+
+Scaled Laplacian matrix of graph `g`,
+defined as ``\hat{L} = \frac{2}{\lambda_{max}} L - I`` where ``L`` is the normalized Laplacian matrix.
+
+# Arguments
+
+- `g`: should be a adjacency matrix, `FeaturedGraph`, `SimpleGraph`, `SimpleDiGraph` (from LightGraphs) or `SimpleWeightedGraph`, `SimpleWeightedDiGraph` (from SimpleWeightedGraphs).
+- `T`: result element type of degree vector; default is the element type of `g` (optional).
+- `dir`: the edge directionality considered (:out, :in, :both).
+"""
+function scaled_laplacian(fg::FeaturedGraph, T::DataType=Int; dir=:out)
+    A = adjacency_matrix(fg, T; dir=dir)
+    @assert issymmetric(A) "scaled_laplacian only works with symmetric matrices"
+    E = eigen(Symmetric(A)).values
+    degs = vec(sum(A; dims=2))
+    inv_sqrtD = Diagonal(inv.(sqrt.(degs)))
+    Lnorm = I - inv_sqrtD * A * inv_sqrtD
+    return  2 / maximum(E) * Lnorm - I
+end
+
+
+function add_self_loop!(adj::AbstractVector{<:AbstractVector})
+    for i = 1:length(adj)
+        i in adj[i] || push!(adj[i], i)
     end
-    A = adjacency_matrix(g, T; dir=dir)
-    s = sum(A; dims=2)
-    D = Diagonal(vec(s))
-    return D - A
+    adj
 end
 
-# TODO Do we need a separate package just for laplacians?
-GraphLaplacians.scaled_laplacian(fg::FeaturedGraph, T::DataType) =
-    scaled_laplacian(adjacency_matrix(fg, T))
-GraphLaplacians.normalized_laplacian(fg::FeaturedGraph, T::DataType; kws...) =
-    normalized_laplacian(adjacency_matrix(fg, T); kws...)
+# # TODO Do we need a separate package just for laplacians?
+# GraphLaplacians.scaled_laplacian(fg::FeaturedGraph, T::DataType) =
+#     scaled_laplacian(adjacency_matrix(fg, T))
+# GraphLaplacians.normalized_laplacian(fg::FeaturedGraph, T::DataType; kws...) =
+#     normalized_laplacian(adjacency_matrix(fg, T); kws...)
+
+
+@non_differentiable normalized_laplacian(x...)
+@non_differentiable scaled_laplacian(x...)
+@non_differentiable add_self_loop!(x...)

src/layers/conv.jl

@@ -247,10 +247,7 @@ update_batch_edge(g::GATConv, adj, E::AbstractMatrix, X::AbstractMatrix, u) = up
 
 function update_batch_edge(g::GATConv, adj, X::AbstractMatrix)
     n = size(adj, 1)
-    # a vertex must always receive a message from itself
-    Zygote.ignore() do
-        GraphLaplacians.add_self_loop!(adj, n)
-    end
+    add_self_loop!(adj)
     mapreduce(i -> apply_batch_message(g, i, adj[i], X), hcat, 1:n)
 end