Changed LightGraphs to Graphs

jarbus · yuehhua · commit edece6579ec0 · 2021-11-18T22:02:19.000+08:00
Conflicts:
	Project.toml

Conflicts:
	test/runtests.jl
diff --git a/README.md b/README.md
@@ -34,7 +34,7 @@ GeometricFlux handles graph data (the topology plus node/vertex/graph features)
 thanks to the type `FeaturedGraph`.
 
 A `FeaturedGraph` can be constructed out of 
-adjacency matrices, adjacency lists, LightGraphs' types...
+adjacency matrices, adjacency lists, Graphs' types...
 
 ```julia
 fg = FeaturedGraph(adj_list)   
diff --git a/docs/src/basics/passgraph.md b/docs/src/basics/passgraph.md
@@ -10,7 +10,7 @@ A static graph is used to reduce redundant computation during passing through la
 GCNConv(adj_mat, feat=>h1, relu)
 ```
 
-`Simple(Di)Graph`, `SimpleWeighted(Di)Graph` or `Meta(Di)Graph` provided by the packages LightGraphs, SimpleWeightedGraphs and MetaGraphs, respectively, are valid arguments for passing as a static graph to this layer. An adjacency list is also accepted in the type of `Vector{Vector}` is also accepted.
+`Simple(Di)Graph`, `SimpleWeighted(Di)Graph` or `Meta(Di)Graph` provided by the packages Graphs, SimpleWeightedGraphs and MetaGraphs, respectively, are valid arguments for passing as a static graph to this layer. An adjacency list is also accepted in the type of `Vector{Vector}` is also accepted.
 
 ## Variable graph
 
@@ -20,7 +20,7 @@ Variable graphs are supported through `FeaturedGraph`, which contains both the g
 FeaturedGraph(adj_mat, features)
 ```
 
-`Simple(Di)Graph`, `SimpleWeighted(Di)Graph` or `Meta(Di)Graph` provided by the packages LightGraphs, SimpleWeightedGraphs and MetaGraphs, respectively, are acceptable for constructing a `FeaturedGraph`. An adjacency list is also accepted, too.
+`Simple(Di)Graph`, `SimpleWeighted(Di)Graph` or `Meta(Di)Graph` provided by the packages Graphs, SimpleWeightedGraphs and MetaGraphs, respectively, are acceptable for constructing a `FeaturedGraph`. An adjacency list is also accepted, too.
 
 ## Cached graph in layers
 
diff --git a/examples/gae.jl b/examples/gae.jl
@@ -6,8 +6,8 @@ using Flux: @epochs
 using JLD2
 using Statistics: mean
 using SparseArrays
-using LightGraphs.SimpleGraphs
-using LightGraphs: adjacency_matrix
+using Graphs.SimpleGraphs
+using Graphs: adjacency_matrix
 using CUDA
 
 @load "data/cora_features.jld2" features
diff --git a/examples/gat.jl b/examples/gat.jl
@@ -6,8 +6,8 @@ using Flux.Data: DataLoader
 using JLD2
 using Statistics: mean
 using SparseArrays
-using LightGraphs.SimpleGraphs
-using LightGraphs: adjacency_matrix
+using Graphs.SimpleGraphs
+using Graphs: adjacency_matrix
 using CUDA
 
 @load "data/cora_features.jld2" features
diff --git a/examples/gcn.jl b/examples/gcn.jl
@@ -5,8 +5,8 @@ using Flux: @epochs
 using JLD2
 using Statistics
 using SparseArrays
-using LightGraphs.SimpleGraphs
-using LightGraphs: adjacency_matrix
+using Graphs.SimpleGraphs
+using Graphs: adjacency_matrix
 using CUDA
 using Random
 
diff --git a/examples/gde.jl b/examples/gde.jl
@@ -2,7 +2,7 @@ using GeometricFlux, Flux, JLD2, SparseArrays, DiffEqFlux, DifferentialEquations
 using Flux: onehotbatch, onecold, logitcrossentropy, throttle
 using Flux: @epochs
 using Statistics: mean
-using LightGraphs: adjacency_matrix
+using Graphs: adjacency_matrix
 
 # Load the dataset
 @load "data/cora_features.jld2" features
diff --git a/examples/gde_gpu.jl b/examples/gde_gpu.jl
@@ -2,7 +2,7 @@ using GeometricFlux, Flux, JLD2, SparseArrays, DiffEqFlux, DifferentialEquations
 using Flux: onehotbatch, onecold, logitcrossentropy, throttle
 using Flux: @epochs
 using Statistics: mean
-using LightGraphs: adjacency_matrix
+using Graphs: adjacency_matrix
 using CUDA
 
 # Load the dataset
diff --git a/examples/vgae.jl b/examples/vgae.jl
@@ -7,8 +7,8 @@ using Flux: @epochs
 using JLD2
 using Statistics
 using SparseArrays
-using LightGraphs.SimpleGraphs
-using LightGraphs: adjacency_matrix
+using Graphs.SimpleGraphs
+using Graphs: adjacency_matrix
 
 @load "data/cora_features.jld2" features
 @load "data/cora_graph.jld2" g
diff --git a/paper/paper.tex b/paper/paper.tex
@@ -139,7 +139,7 @@ \section{Static and Variable Graph Support}
 processed for efficiency in prior during constructing GNN layer and variable graphs are
 processed during network training time. In this framework, graph network block is designed as
 fundamental layers. Each layer accepts input of node features, edge features, global features
-and graph. Graph structures from LightGraphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl
+and graph. Graph structures from Graphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl
 are accepted. FeaturedGraph is designed as generic data structure for containing different
 kinds of features and graph structure.
 
@@ -157,16 +157,16 @@ \section{Compatible with Flux Layers}
 \section{Integration with JuliaGraphs}
 
 JuliaGraphs already forms a whole ecosystem for graph operations, graph visualization and
-solving problems in graph theory. LightGraphs.jl and SimpleWeightedGraphs.jl provides the
+solving problems in graph theory. Graphs.jl and SimpleWeightedGraphs.jl provides the
 graph construction and representation in unweighted and weighted graphs.
 MetaGraphs.jl provides user the chance to assign properties on nodes, edges or global graph.
 Integration with JuliaGraphs ecosystem provides more ways to assign graph to model and
 reduce the effort of transformation between data types. Graph representation constructed
-from LightGraphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl are accepted in construction
+from Graphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl are accepted in construction
 of geometric deep learning model in GeometricFlux. Under construction of graph convolutional
 layer, a static graph is accepted as one of arguments of neural network layer during
 configuring model. In the context of using variable graph, FeaturedGraph also accepts graph
-representation constructed from LightGraphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl
+representation constructed from Graphs.jl, SimpleWeightedGraphs.jl and MetaGraphs.jl
 and can be fed as sample directly to model. This feature accepts graph representations
 from JuliaGraphs ecosystem to geometric deep learning model.
 
diff --git a/perf/perf.jl b/perf/perf.jl
@@ -1,4 +1,4 @@
-using Flux, GeometricFlux, LightGraphs, BenchmarkTools, CUDA
+using Flux, GeometricFlux, Graphs, BenchmarkTools, CUDA
 using DataFrames, Statistics, JLD2, SparseArrays
 
 BenchmarkTools.ratio(::Missing, x) = Inf

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-using Flux, GeometricFlux, LightGraphs, BenchmarkTools, CUDA`
	`1`	`+using Flux, GeometricFlux, Graphs, BenchmarkTools, CUDA`
`2`	`2`	`using DataFrames, Statistics, JLD2, SparseArrays`
`3`	`3`
`4`	`4`	`BenchmarkTools.ratio(::Missing, x) = Inf`