Add Louvain community detection algorithm

docs/make.jl

@@ -94,6 +94,7 @@ makedocs(;
         canonical="https://gdalle.github.io/Graphs.jl",
     ),
     sitename="Graphs.jl",
+    checkdocs=:public,
     doctest=false,
     expandfirst=[],
     pages=[

docs/src/algorithms/community.md

@@ -19,6 +19,7 @@ Pages = [
     "community/clustering.jl",
     "community/core-periphery.jl",
     "community/label_propagation.jl",
+    "community/louvain.jl",
     "community/modularity.jl",
     "community/rich_club.jl",
 ]

src/Graphs.jl

@@ -18,7 +18,7 @@ using DataStructures:
     BinaryMaxHeap,
     BinaryMinHeap,
     Stack
-using LinearAlgebra: I, Symmetric, diagm, eigen, eigvals, norm, rmul!, tril, triu
+using LinearAlgebra: I, Symmetric, diagind, diagm, eigen, eigvals, norm, rmul!, tril, triu
 import LinearAlgebra: Diagonal, issymmetric, mul!
 using Random:
     AbstractRNG,
@@ -315,6 +315,7 @@ export
     global_clustering_coefficient,
     triangles,
     label_propagation,
+    louvain,
     maximal_cliques,
     clique_percolation,
     assortativity,
@@ -528,6 +529,7 @@ include("centrality/eigenvector.jl")
 include("centrality/radiality.jl")
 include("community/modularity.jl")
 include("community/label_propagation.jl")
+include("community/louvain.jl")
 include("community/core-periphery.jl")
 include("community/clustering.jl")
 include("community/cliques.jl")

src/community/louvain.jl

@@ -0,0 +1,271 @@
+"""
+    louvain(g, distmx=weights(g), γ=1; max_moves::Integer=1000, max_merges::Integer=1000, move_tol::Real=10e-10, merge_tol::Real=10e-10, rng=nothing, seed=nothing)
+
+Community detection using the louvain algorithm. Finds a partition of the vertices that
+attempts to maximize the modularity. Returns a vector of community ids.
+
+### Optional Arguments
+- `distmx=weights(g)`: distance matrix for weighted graphs
+- `γ=1.0`: where `γ > 0` is a resolution parameter. Higher resolutions lead to more
+    communities, while lower resolutions lead to fewer communities. Where `γ=1.0` it
+    leads to the traditional definition of the modularity.
+- `max_moves=1000`: maximum number of rounds moving vertices before merging.
+- `max_merges=1000`: maximum number of merges.
+- `move_tol=10e-10`: necessary increase of modularity to move a vertex.
+- `merge_tol=10e-10`: necessary increase of modularity in the move stage to merge.
+- `rng=nothing`: rng to use for reproducibility. May only pass one of rng or seed.
+- `seed=nothing`: seed to use for reproducibility. May only pass one of rng or seed.
+
+### References
+- [Vincent D Blondel et al J. Stat. Mech. (2008) P10008][https://doi.org/10.1088/1742-5468/2008/10/P10008]
+- [Nicolas Dugué, Anthony Perez. Directed Louvain : maximizing modularity in directed networks.][https://hal.science/hal-01231784/document]
+
+# Examples 
+```jldoctest
+julia> using Graphs
+
+julia> barbell = blockdiag(complete_graph(3), complete_graph(3));
+
+julia> add_edge!(barbell, 1, 4);
+
+julia> louvain(barbell)
+6-element Vector{Int64}:
+ 1
+ 1
+ 1
+ 2
+ 2
+ 2
+
+julia> louvain(barbell, γ=0.01)
+6-element Vector{Int64}:
+ 1
+ 1
+ 1
+ 1
+ 1
+ 1
+```
+"""
+function louvain(
+    g::AbstractGraph{T};
+    γ=1.0,
+    distmx::AbstractArray{<:Number}=weights(g),
+    max_moves::Integer=1000,
+    max_merges::Integer=1000,
+    move_tol::Real=10e-10,
+    merge_tol::Real=10e-10,
+    rng::Union{Nothing,AbstractRNG}=nothing,
+    seed::Union{Nothing,Integer}=nothing,
+) where {T}
+    rng = rng_from_rng_or_seed(rng, seed)
+    n = nv(g)
+    if n == 0
+        return T[]
+    end
+
+    @debug "Running louvain with parameters γ=$(γ), max_moves=$(max_moves), " *
+        "max_merges=$(max_merges), move_tol=$(move_tol), merge_tol=$(merge_tol)"
+
+    actual_coms = collect(one(T):nv(g))
+    current_coms = copy(actual_coms)
+    # actual_coms is always of length nv(g) and holds the current com for each v in g
+    # current_coms is for the current graph; after merges it will be smaller than nv(g)
+
+    for iter in 0:max_merges
+        current_modularity = modularity(g, current_coms; distmx=distmx, γ=γ)
+        @debug "Merge iteration $(iter). Current modularity is $(current_modularity)"
+        louvain_move!(g, γ, current_coms, rng, distmx, max_moves, move_tol)
+        # remap communities to 1-nc
+        com_map = Dict(old => new for (new, old) in enumerate(unique(current_coms)))
+        for i in eachindex(actual_coms)
+            actual_coms[i] = com_map[current_coms[actual_coms[i]]]
+        end
+        @debug "Communities after moving in iteration $(iter): $(actual_coms)"
+        for i in eachindex(current_coms)
+            current_coms[i] = com_map[current_coms[i]]
+        end
+
+        # Stop if modularity gain is too small
+        new_modularity = modularity(g, current_coms; distmx=distmx, γ=γ)
+        @debug "New modularity is $(new_modularity) for a gain of $(new_modularity -
+        current_modularity)"
+        if new_modularity - current_modularity < merge_tol
+            break
+        end
+        g, distmx = louvain_merge(g, current_coms, distmx)
+        if nv(g) == 1 # nothing left to merge
+            break
+        end
+        current_coms = collect(one(T):nv(g))
+    end
+    return actual_coms
+end
+
+"""
+    louvain_move!(g, γ, c, rng, distmx=weights(g), max_moves=1000, move_tol=10e-10)
+
+The move stage of the louvain algorithm.
+"""
+function louvain_move!(
+    g, γ, c, rng, distmx=weights(g), max_moves::Integer=1000, move_tol::Real=10e-10
+)
+    vertex_order = shuffle!(rng, collect(vertices(g)))
+    nc = maximum(c)
+
+    # Compute graph and community volumes
+    m = 0
+    c_vols = zeros(eltype(distmx), ((is_directed(g) ? 2 : 1), nc))
+    # if is_directed use row 1 for in and 2 for out
+    for e in edges(g)
+        m += distmx[src(e), dst(e)]
+        c_vols[1, c[src(e)]] += distmx[src(e), dst(e)]
+        if is_directed(g)
+            c_vols[2, c[dst(e)]] += distmx[src(e), dst(e)]
+        else
+            c_vols[1, c[dst(e)]] += distmx[src(e), dst(e)]
+        end
+    end
+
+    for _ in 1:max_moves
+        last_change = nothing
+        for v in vertex_order
+            if v == last_change  # stop if we see each vertex and no movement
+                return nothing
+            end
+            potential_coms = unique(c[u] for u in all_neighbors(g, v))
+            filter!(!=(c[v]), potential_coms)
+            @debug "Moving vertex $(v) from com $(c[v]) to potential_coms $(potential_coms)"
+            if isempty(potential_coms)  # Continue if there are no other neighboring coms
+                continue
+            end
+            shuffle!(rng, potential_coms)  # Break ties randomly by first com
+
+            #Remove vertex degrees from current community
+            out_degree = sum(
+                u == v ? 2distmx[v, u] : distmx[v, u] for u in outneighbors(g, v)
+            )
+            c_vols[1, c[v]] -= out_degree
+
+            in_degree = 0.0  # defined outside to keep JET.jl happy
+            if is_directed(g)
+                in_degree = sum(
+                    u == v ? 2distmx[v, u] : distmx[v, u] for u in inneighbors(g, v)
+                )
+                c_vols[2, c[v]] -= in_degree
+            end
+
+            # Compute loss in modularity by removing vertex
+            loss = ΔQ(g, γ, distmx, c, v, m, c[v], c_vols)
+            @debug "Q loss of removing vertex $(v) from its community: $(loss)"
+            # Compute gain by moving to alternate neighboring community
+            this_ΔQ = c_potential -> ΔQ(g, γ, distmx, c, v, m, c_potential, c_vols)
+            best_ΔQ, best_com_id = findmax(this_ΔQ, potential_coms)
+            best_com = potential_coms[best_com_id]
+            @debug "Best move is to $(best_com) with Q gain of $(best_ΔQ)"
+            if best_ΔQ - loss > move_tol
+                c[v] = best_com
+                c_vols[1, best_com] += out_degree
+                if is_directed(g)
+                    c_vols[2, best_com] += in_degree
+                end
+                last_change = v
+                @debug "Moved vertex $(v) to community $(best_com)"
+            else
+                c_vols[1, c[v]] += out_degree
+                if is_directed(g)
+                    c_vols[2, c[v]] += in_degree
+                end
+                @debug "Insufficient Q gain, vertex $(v) stays in community $(c[v])"
+            end
+        end
+        if isnothing(last_change) # No movement
+            return nothing
+        end
+    end
+end
+
+"""
+    ΔQ(g, γ, distmx, c, v, m, c_potential, c_vols)
+
+Compute the change in modularity when adding vertex v a potential community.
+"""
+function ΔQ(g, γ, distmx, c, v, m, c_potential, c_vols)
+    if is_directed(g)
+        out_degree = 0
+        com_out_degree = 0
+        for u in outneighbors(g, v)
+            out_degree += distmx[v, u]
+            if c[u] == c_potential || u == v
+                com_out_degree += distmx[v, u]
+            end
+        end
+
+        in_degree = 0
+        com_in_degree = 0
+        for u in inneighbors(g, v)
+            in_degree += distmx[u, v]
+            if c[u] == c_potential || u == v
+                com_in_degree += distmx[u, v]
+            end
+        end
+
+        # Singleton special case
+        if c_vols[1, c_potential] == 0 && c_vols[2, c_potential] == 0
+            return (com_in_degree+com_out_degree)/m - γ*2(in_degree + out_degree)/m^2
+        end
+        return (com_in_degree+com_out_degree)/m -
+               γ*(in_degree*c_vols[1, c_potential]+out_degree*c_vols[2, c_potential])/m^2
+    else
+        degree = 0
+        com_degree = 0
+        for u in neighbors(g, v)
+            degree += u == v ? 2distmx[u, v] : distmx[u, v]
+            if u == v
+                com_degree += 2distmx[u, v]
+            elseif c[u] == c_potential
+                com_degree += distmx[u, v]
+            end
+        end
+        # Singleton special case
+        if c_vols[1, c_potential] == 0
+            return com_degree/2m - γ*(degree/2m)^2
+        end
+        return com_degree/2m - γ*degree*c_vols[1, c_potential]/2m^2
+    end
+end
+
+"""
+    louvain_merge(g, c, distmx)
+
+Merge stage of the louvain algorithm.
+"""
+function louvain_merge(g::AbstractGraph{T}, c, distmx) where {T}
+    # c is assumed to be 1:nc
+    nc = maximum(c)
+    new_distmx = Dict{Tuple{T,T},eltype(distmx)}()
+    new_graph = is_directed(g) ? SimpleDiGraph{T}(nc) : SimpleGraph{T}(nc)
+    for e in edges(g)
+        new_src = c[src(e)]
+        new_dst = c[dst(e)]
+        if haskey(new_distmx, (new_src, new_dst))
+            new_distmx[(new_src, new_dst)] += distmx[src(e), dst(e)]
+        else
+            new_distmx[(new_src, new_dst)] = distmx[src(e), dst(e)]
+        end
+        add_edge!(new_graph, new_src, new_dst)
+    end
+
+    # Convert new_distmx Dict to SparseArray
+    r = [k[1] for k in keys(new_distmx)]
+    c = [k[2] for k in keys(new_distmx)]
+    v = [v for v in values(new_distmx)]
+    new_distmx = sparse(r, c, v, nc, nc)
+
+    if !is_directed(new_graph)
+        new_distmx = new_distmx + transpose(new_distmx)
+        new_distmx[diagind(new_distmx)] ./= 2
+    end
+
+    return new_graph, new_distmx
+end