Assortativity extended to graphs with attributes

src/community/assortativity.jl

@@ -1,21 +1,35 @@
 """
  assortativity(g)
+ assortativity(g, attributes)
 
 Return the [assortativity coefficient](https://en.wikipedia.org/wiki/Assortativity)
 of graph `g`, defined as the Pearson correlation of excess degree between
 the end vertices of all the edges of the graph.
 
+If `attributes` is provided, Pearson correlation is calculated
+from atrribute values associated to each vertex and stored in
+vector `attributes`.
+
 The excess degree is equal to the degree of linked vertices minus one,
 i.e. discounting the edge that links the pair.
 In directed graphs, the paired values are the out-degree of source vertices
 and the in-degree of destination vertices.
 
+# Arguments
+- `attributes` (optional) is a vector that associates to each vertex `i` a scalar value
+`attributes[i]`.
+
 # Examples
 ```jldoctest
 julia> using LightGraphs
 
 julia> assortativity(star_graph(4))
 -1.0
+
+julia> attributes = [-1., -1., 1., 1.]
+
+julia> assortativity(star_graph(4),attributes)
+-0.5
 ```
 """
 function assortativity(g::AbstractGraph{T}) where T
@@ -34,18 +48,34 @@ function assortativity(g::AbstractGraph{T}) where T
  return assortativity_coefficient(g, sjk, sj, sk, sjs, sks, nue)
 end
 
+function assortativity(g::AbstractGraph{T}, attributes::Vector{N}) where {T,N<:Number}
+ P = promote_type(Int64, N) # at least Int64 to reduce risk of overflow
+ nue = ne(g)
+ sjk = sj = sk = sjs = sks = zero(P)
+ for d in edges(g)
+ j = attributes[src(d)]
+ k = attributes[dst(d)]
+ sjk += j*k
+ sj += j
+ sk += k
+ sjs += j^2
+ sks += k^2
+ end
+ return assortativity_coefficient(g, sjk, sj, sk, sjs, sks, nue)
+end
+
 #=
-assortativity coefficient for directed graphs: 
-see equation (21) in M. E. J. Newman: Mixing patterns in networks, Phys. Rev. E 67, 026126 (2003), 
+assortativity coefficient for directed graphs:
+see equation (21) in M. E. J. Newman: Mixing patterns in networks, Phys. Rev. E 67, 026126 (2003),
 http://arxiv.org/abs/cond-mat/0209450
 =#
 @traitfn function assortativity_coefficient(g::::IsDirected, sjk, sj, sk, sjs, sks, nue)
  return (sjk - sj*sk/nue) / sqrt((sjs - sj^2/nue)*(sks - sk^2/nue))
 end
 
 #=
-assortativity coefficient for undirected graphs: 
-see equation (4) in M. E. J. Newman: Assortative mixing in networks, Phys. Rev. Lett. 89, 208701 (2002), 
+assortativity coefficient for undirected graphs:
+see equation (4) in M. E. J. Newman: Assortative mixing in networks, Phys. Rev. Lett. 89, 208701 (2002),
 http://arxiv.org/abs/cond-mat/0205405/
 =#
 @traitfn function assortativity_coefficient(g::::(!IsDirected), sjk, sj, sk, sjs, sks, nue)

test/community/assortativity.jl

@@ -20,4 +20,28 @@ using Random, Statistics
  y = [indegree(g, dst(d))-1 for d in edges(g)]
  @test @inferred assort ≈ cor([x;y], [y;x])
  end
+
+ # Test definition of assortativity as Pearson correlation coefficient
+ # between some scalar attributes characterising each vertex
+ @testset "Small graphs with attributes" for n = 5:2:11
+ g = grid([1, n])
+ attributes = [(-1.)^v for v in 1:nv(g)]
+ @test @inferred assortativity(g, attributes) ≈ -1.
+ end
+ @testset "Directed ($seed) with attributes" for seed in [1, 2, 3], (_n, _ne) in [(14, 18), (10, 22), (7, 16)]
+ g = erdos_renyi(_n, _ne; is_directed=true, seed=seed)
+ attributes = randn(_n)
+ assort = assortativity(g, attributes)
+ x = [attributes[src(d)] for d in edges(g)]
+ y = [attributes[dst(d)] for d in edges(g)]
+ @test @inferred assort ≈ cor(x, y)
+ end
+ @testset "Undirected ($seed) with attributes" for seed in [1, 2, 3], (_n, _ne) in [(14, 18), (10, 22), (7, 16)]
+ g = erdos_renyi(_n, _ne; is_directed=false, seed=seed)
+ attributes = randn(_n)
+ assort = assortativity(g, attributes)
+ x = [attributes[src(d)] for d in edges(g)]
+ y = [attributes[dst(d)] for d in edges(g)]
+ @test @inferred assort ≈ cor([x;y], [y;x])
+ end
 end