COO FeaturedGraph

Project.toml

@@ -5,12 +5,10 @@ version = "0.7.6"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+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"
@@ -23,11 +21,8 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 [compat]
 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"

docs/src/abstractions/msgpass.md

@@ -20,20 +20,20 @@ A message function accepts feature vector representing node state `x_i`, feature
 Messages from message function are aggregated by an aggregate function. An aggregated message is passed to update function for node-level computation. An aggregate function is given by the following:
 
 ```
-propagate(mp, fg::FeaturedGraph, aggr::Symbol=:add)
+propagate(mp, fg::FeaturedGraph; aggr::Symbol=+)
 ```
 
-`propagate` function calls the whole message passing layer. `fg` acts as an input for message passing layer and `aggr` represents assignment of aggregate function to `propagate` function. `:add` represents an aggregate function of addition of all messages.
+`propagate` function calls the whole message passing layer. `fg` acts as an input for message passing layer and `aggr` represents assignment of aggregate function to `propagate` function. `+` represents an aggregate function of addition of all messages.
 
 The following `aggr` are available aggregate functions:
 
-`:add`: sum over all messages
-`:sub`: negative of sum over all messages
-`:mul`: multiplication over all messages
-`:div`: inverse of multiplication over all messages
-`:max`: the maximum of all messages
-`:min`: the minimum of all messages
-`:mean`: the average of all messages
+`+`: sum over all messages
+`-`: negative of sum over all messages
+`*`: multiplication over all messages
+`/`: inverse of multiplication over all messages
+`max`: the maximum of all messages
+`min`: the minimum of all messages
+`mean`: the average of all messages
 
 ## Update function
 

src/GeometricFlux.jl

@@ -1,22 +1,30 @@
 module GeometricFlux
 
+using NNlib: similar
+using ChainRulesCore: eltype, reshape
+using LinearAlgebra: similar
 using Statistics: mean
-using LinearAlgebra: Adjoint, norm, Transpose
-using Reexport
+using LinearAlgebra
 
 using CUDA
-using FillArrays: Fill
 using Flux
 using Flux: glorot_uniform, leakyrelu, GRUCell, @functor
 using NNlib, NNlibCUDA
-using GraphLaplacians
-@reexport using GraphSignals
-using LightGraphs
 using Zygote
+using ChainRulesCore
+import LightGraphs
+using LightGraphs: AbstractGraph, outneighbors, inneighbors, is_directed, ne, nv, 
+                  adjacency_matrix, degree
 
 export
-    # layers/gn
-    GraphNet,
+    # featured_graph
+    FeaturedGraph,
+    edge_index,
+    node_feature, edge_feature, global_feature,
+    adjacency_list, normalized_laplacian, scaled_laplacian,
+
+    # from LightGraphs
+    ne, nv, adjacency_matrix, 
 
     # layers/msgpass
     MessagePassing,
@@ -44,26 +52,20 @@ export
     sample,
 
     # layer/selector
-    bypass_graph,
-
-    # utils
-    generate_cluster
+    bypass_graph
 
+include("featured_graph.jl")  
 include("datasets.jl")
 
 include("utils.jl")
 
-include("layers/gn.jl")
 include("layers/msgpass.jl")
 
 include("layers/conv.jl")
 include("layers/pool.jl")
 include("models.jl")
 include("layers/misc.jl")
 
-include("cuda/msgpass.jl")
-include("cuda/conv.jl")
-
 using .Datasets