Merge pull request #6 from storopoli/master

Move from LightGraphs to Graphs

Author: matbesancon

.gitignore

@@ -1,3 +1,10 @@
+.DS_Store
 *.jl.cov
-*.jl.*.cov
 *.jl.mem
+.vscode
+docs/build/
+docs/site/
+benchmark/.results/*
+benchmark/.tune.jld
+*.cov
+Manifest.toml

Project.toml

@@ -0,0 +1,23 @@
+name = "CommunityDetection"
+uuid = "d427f087-d71a-5a1b-ace0-b93392eea9ff"
+authors = ["JuliaGraphs"]
+version = "0.2.0"
+
+[deps]
+ArnoldiMethod = "ec485272-7323-5ecc-a04f-4719b315124d"
+Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+
+[compat]
+ArnoldiMethod = "0.2"
+Clustering = "0.14"
+Graphs = "1.4"
+julia = "1"
+
+[extras]
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["SparseArrays", "Test"]

README.md

@@ -5,4 +5,4 @@
 
 ### CommunityDetection.jl 
 
-Implements community detection for [LightGraphs.jl](https://github.com/JuliaGraphs/LightGraphs.jl). Both Nonbacktracking and Bethe Hessian detection are supported.
+Implements community detection for [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl). Both Nonbacktracking and Bethe Hessian detection are supported.

REQUIRE

@@ -1,5 +1,5 @@
 module CommunityDetection
-using LightGraphs
+using Graphs
 using ArnoldiMethod: LR, SR
 using LinearAlgebra: I, Diagonal
 using Clustering: kmeans
@@ -57,7 +57,7 @@ See `Nonbacktracking` for details.
 """
 function nonbacktrack_embedding(g::AbstractGraph, k::Int)
     B = Nonbacktracking(g)
-    λ, eigv = LightGraphs.LinAlg.eigs(B, nev=k+1, which=LR())
+    λ, eigv = Graphs.LinAlg.eigs(B, nev=k+1, which=LR())
     ϕ = zeros(ComplexF32, nv(g), k-1)
     # TODO decide what to do with the stationary distribution ϕ[:,1]
     # this code just throws it away in favor of eigv[:,2:k+1].
@@ -93,7 +93,7 @@ function community_detection_bethe(g::AbstractGraph, k::Int=-1; kmax::Int=15)
     Hr = Matrix((r^2-1)*I, nv(g), nv(g)) - r*A + D;
     #Hmr = Matrix((r^2-1)*I, nv(g), nv(g)) + r*A + D;
     k >= 1 && (kmax = k)
-    λ, eigv = LightGraphs.LinAlg.eigs(Hr, which=SR(), nev=min(kmax, nv(g)))
+    λ, eigv = Graphs.LinAlg.eigs(Hr, which=SR(), nev=min(kmax, nv(g)))
 
     # TODO eps() is chosen quite arbitrarily here, because some of eigenvalues
     # don't convert exactly to zero as they should. Some analysis could show

src/CommunityDetection.jl

@@ -1,5 +1,5 @@
 using CommunityDetection
-using LightGraphs
+using Graphs
 using LinearAlgebra: I, norm
 using ArnoldiMethod: LR
 using SparseArrays: sparse
@@ -15,7 +15,7 @@ return : a matrix ϕ where ϕ[:,i] are the coordinates for vertex i.
 """
 function nonbacktrack_embedding_dense(g::AbstractGraph, k::Int)
     B, edgeid = non_backtracking_matrix(g)
-    λ, eigv = LightGraphs.LinAlg.eigs(B, nev=k+1, which=LR())
+    λ, eigv = Graphs.LinAlg.eigs(B, nev=k+1, which=LR())
     ϕ = zeros(ComplexF32, k-1, nv(g))
     # TODO decide what to do with the stationary distribution ϕ[:,1]
     # this code just throws it away in favor of eigv[:,2:k+1].
@@ -36,7 +36,7 @@ end
 @testset "CommunityDetection" begin
 
 n = 10; k = 5
-pg = PathGraph(n)
+pg = path_graph(n)
 ϕ1 = CommunityDetection.nonbacktrack_embedding(pg, k)'
 
 nbt = Nonbacktracking(pg)
@@ -61,7 +61,7 @@ z = B * x
 #check that this recovers communities in the path of cliques
 @testset "community_detection_nback(z, k)" begin
     n=10
-    g10 = CompleteGraph(n)
+    g10 = complete_graph(n)
     z = copy(g10)
     for k=2:5
         z = blockdiag(z, g10)
@@ -80,7 +80,7 @@ end
 
 @testset "community_detection_bethe(z, k)" begin
     n=10
-    g10 = CompleteGraph(n)
+    g10 = complete_graph(n)
     z = copy(g10)
     for k=2:5
         z = blockdiag(z, g10)
@@ -101,7 +101,7 @@ end
 
 @testset "community_detection_bethe(z)" begin
     n=10
-    g10 = CompleteGraph(n)
+    g10 = complete_graph(n)
     z = copy(g10)
     for k=2:5
         z = blockdiag(z, g10)