Assortativity extended to graphs with attributes

[vboussange]

Hi there, I propose an extension of method assortativity to account for scalar properties of vertices.
This is also done in networkX (python).

"""
assortativity(g,labels)
Similar to assortativity(g) except that Pearson correlation is calculated between some values associated to each node and stored in
labels.

Arguments

• labels is a dictionary that associates to each vertex index a scalar value

Examples

julia> using LightGraphs

julia> labels = Dict(collect(1:4) .=> [-1., -1., 1., 1.])

julia> assortativity(star_graph(4),labels)
-0.5

"""

[codecov]

Codecov Report

Merging #1547 (cd7a842) into master (5d2b80a) will increase coverage by 0.00%.
The diff coverage is 100.00%.

❗ Current head cd7a842 differs from pull request most recent head 91a3f08. Consider uploading reports for the commit 91a3f08 to get more accurate results

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[sbromberger]

Thanks for these PRs, @vboussange ! In order for us to merge them, they will need comprehensive tests.

Also, a formal review is forthcoming, but why are you using a Dict for vertex indices instead of a Vector{T}? Vectors are much more efficient.

[vboussange]

Thanks for these PRs, @vboussange ! In order for us to merge them, they will need comprehensive tests.

Also, a formal review is forthcoming, but why are you using a Dict for vertex indices instead of a Vector{T}? Vectors are much more efficient.

@sbromberger Thanks for your comments. I was using Dict because of personal historical contingencies. You are right - Vector makes much more sense. I have made a new commit with the updated function, added some tests, and modified the documentation accordingly.

[sbromberger]

sorry for the delay in review - I should be able to get to this this week.

[sbromberger]

Best of intentions, etc. Sorry for the delay. Reviewing now.