fixed adjacency matrix (#745)

CONTRIBUTING.md

@@ -49,3 +49,4 @@ function inner!(storage::AbstractVector{Int}, g::AbstractGraph, v::Integer)
 end
 ```
 This allows us to reuse the memory and improve performance.
+

src/linalg/spectral.jl

@@ -18,7 +18,6 @@ function adjacency_matrix(g::AbstractGraph, T::DataType=Int; dir::Symbol=:out)
     # see below - we iterate over columns. That's why we take the
     # "opposite" neighbor function. It's faster than taking the transpose
     # at the end.
-
     if (dir == :out)
         _adjacency_matrix(g, T, in_neighbors, 1)
     elseif (dir == :in)
@@ -38,10 +37,11 @@ end
 function _adjacency_matrix(g::AbstractGraph, T::DataType, neighborfn::Function, nzmult::Int=1)
     n_v = nv(g)
     nz = ne(g) * (is_directed(g) ? 1 : 2) * nzmult
-    colpt = ones(Int, n_v + 1)
-
-    rowval = sizehint!(Vector{Int}(), nz)
-    selfloops = Vector{Int}()
+    U = eltype(g)
+    colpt = ones(U, n_v + 1)
+
+    rowval = sizehint!(Vector{U}(), nz)
+    selfloops = Vector{U}()
     for j in 1:n_v  # this is by column, not by row.
         if has_edge(g, j, j)
             push!(selfloops, j)
@@ -77,11 +77,12 @@ For undirected graphs, `dir` defaults to `:out`; for directed graphs,
 `dir` defaults to `:both`. 
 """
 function laplacian_matrix(g::AbstractGraph, T::DataType=Int; dir::Symbol=:unspec)
+    U = eltype(g)
     if dir == :unspec
         dir = is_directed(g) ? :both : :out
     end
     A = adjacency_matrix(g, T; dir=dir)
-    D = spdiagm(sum(A, 2)[:])
+    D = convert(SparseMatrixCSC{T, U}, spdiagm(sum(A, 2)[:]))
     return D - A
 end
 

test/linalg/spectral.jl

@@ -90,10 +90,11 @@ full(nbt::Nonbacktracking) = full(sparse(nbt))
 
       #check properties of the undirected laplacian carry over.
         for dir in [:in, :out, :both]
+            T = eltype(g)
             amat = adjacency_matrix(g, Float64; dir=dir)
             lmat = laplacian_matrix(g, Float64; dir=dir)
-            @test isa(amat, SparseMatrixCSC{Float64,Int64})
-            @test isa(lmat, SparseMatrixCSC{Float64,Int64})
+            @test isa(amat, SparseMatrixCSC{Float64,T})
+            @test isa(lmat, SparseMatrixCSC{Float64,T})
             evals = eigvals(full(lmat))
             @test all(evals .>= -1e-15) # positive semidefinite
             @test (minimum(evals)) ≈ 0 atol = 1e-13