Update docstrings

Co-Authored-By: Pietro Monticone <[email protected]>
Co-Authored-By: Claudio Moroni <[email protected]>

Author: 3 people

docs/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"

docs/make.jl

@@ -1,4 +1,5 @@
 using Revise
+using Distributions
 using Graphs
 using MultilayerGraphs
 using Documenter

docs/src/API.md

@@ -358,7 +358,7 @@ outneighbors(subgraph::AbstractSubGraph, mv::MultilayerVertex)
 neighbors(subgraph::S, v::T) where {T,S<:AbstractSubGraph{T}}
 neighbors(subgraph::AbstractSubGraph, mv::MultilayerVertex)
 edgetype(::S) where {T,U,S<:AbstractSubGraph{T,U}}
-has_edge(subgraph::S, s::MultilayerVertex, d::MultilayerVertex) where { T, S <: AbstractSubGraph{T}}
+has_edge( subgraph::S, s::T, d::T) where {T,S<:AbstractSubGraph{T}}
 add_edge!(subgraph::S, src::T, dst::T; weight::W = nothing, metadata::Union{Tuple, NamedTuple}= NamedTuple()) where {T, U<: Real, W<:Union{ U, Nothing},G<:AbstractGraph{T},S<:AbstractSubGraph{T,U,G}} 
 rem_edge!(subgraph::S, src::T, dst::T) where {T, S<:AbstractSubGraph{T}}
 AbstractLayer

src/abstractmultilayergraph.jl

@@ -780,7 +780,7 @@ degree_variance(mg::AbstractMultilayerGraph) = var(degree(mg))
         norm_factor::Union{Float64,Symbol}=:max
     )
 
-Return the complete multilayer global clustering coefficient, equal to the ratio of realized triplets over all possible triplets, including those whose every or some edges belong to interlayers, normalized by `norm_factor`. If `norm_factor == :max`, then the ratio is normalized by `maximum(mg.array)`. This function does not override Graphs.jl's `global_clustering_coefficient`, since the latter does not consider cliques where two nodes are the same node but in different layers/interlayers. See [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022).
+Return the complete multilayer global clustering coefficient, equal to the ratio of realized triplets over all possible triplets, including those whose every or some edges belong to interlayers, normalized by `norm_factor`. If `norm_factor == :max`, then the ratio is normalized by `maximum(array(weight_tensor(mg)))`, else it is not normalized. This function does not override Graphs.jl's `global_clustering_coefficient`, since the latter does not consider cliques where two nodes are the same node but in different layers/interlayers. See [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022).
 """
 function multilayer_global_clustering_coefficient(
     mg::AbstractMultilayerGraph, norm_factor::Union{Float64,Symbol}=:max
@@ -816,7 +816,7 @@ end
 """
     multilayer_weighted_global_clustering_coefficient(mg::M, norm_factor::Union{Float64, Symbol} = :max) where {M <: AbstractMultilayerGraph}
 
-Return the complete multilayer global clustering coefficient, equal to the ratio of realized triplets over all possible triplets, including those whose every or some edges belong to interlayers, normalized by `norm_factor`. Each triplets contributes for `w[1]` if all of its vertices are in one layer, `w[2]` if its vertices span two layers, and `w[3]` if they span 3 layers. If `norm_factor == :max`, then the ratio is normalized by `maximum(mg.array)`. This function does not override Graphs.jl's `global_clustering_coefficient`, since the latter does not consider cliques where two nodes are the same node but in different layers/interlayers. See [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022).
+Return the complete multilayer global clustering coefficient, equal to the ratio of realized triplets over all possible triplets, including those whose every or some edges belong to interlayers, normalized by `norm_factor`. Each triplets contributes for `w[1]` if all of its vertices are in one layer, `w[2]` if its vertices span two layers, and `w[3]` if they span 3 layers. If `norm_factor == :max`, then the ratio is normalized by `maximum(array(weight_tensor(mg)))`, else it is not normalized. This function does not override Graphs.jl's `global_clustering_coefficient`, since the latter does not consider cliques where two nodes are the same node but in different layers/interlayers. See [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022).
 """
 function multilayer_weighted_global_clustering_coefficient(
     mg::M, w::Vector{Float64}, norm_factor::Union{Float64,Symbol}=:max
@@ -851,7 +851,7 @@ end
         norm_factor::Union{Float64,Symbol}=:max
     )
 
-Return the overlay clustering coefficient as calculated in [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022).
+Return the overlay clustering coefficient as calculated in [De Domenico et al. (2013)](https://doi.org/10.1103/PhysRevX.3.041022). If `norm_factor == :max`, then the ratio is normalized by `maximum(array(weight_tensor(mg)))`, else it is not normalized. 
 """
 function overlay_clustering_coefficient(
     mg::AbstractMultilayerGraph, norm_factor::Union{Float64,Symbol}=:max

src/subgraphs/abstractsubgraph.jl

@@ -203,7 +203,7 @@ Return `true` if there is an edge between `s` and `d`, `false` otherwise.
 Graphs.has_edge( subgraph::AbstractSubGraph, s::MultilayerVertex, d::MultilayerVertex) =  has_edge(subgraph, get_v(subgraph, s), get_v(subgraph, d))
 
 """
-    has_edge(subgraph::S, s::MultilayerVertex, d::MultilayerVertex) where { T, S <: AbstractSubGraph{T}}
+    has_edge( subgraph::S, s::T, d::T) where {T,S<:AbstractSubGraph{T}}
 
 Return `true` if there is an edge between `s` and `d`, `false` otherwise.
 """