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Krastanov merged 3 commits into from
Feb 19, 2026
+50 −9
Merged

Fix modularity when self loops exist #487

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cf8d44b
Fix self loops n modularity
ryandewolfe33 Jan 27, 2026
70e8b80
Format modularity test file
ryandewolfe33 Jan 28, 2026
9c9fdf7
Run formatter on test file
ryandewolfe33 Jan 28, 2026
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18 changes: 12 additions & 6 deletions src/community/modularity.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ julia> barbell = blockdiag(complete_graph(3), complete_graph(3));
julia> add_edge!(barbell, 1, 4);

julia> modularity(barbell, [1, 1, 1, 2, 2, 2])
0.35714285714285715
0.3571428571428571

julia> modularity(barbell, [1, 1, 1, 2, 2, 2], γ=0.5)
0.6071428571428571
Expand Down Expand Up @@ -90,14 +90,20 @@ function modularity(
c2 = c[v]
if c1 == c2
Q += distmx[u, v]
if u == v && !is_directed(g)
#Since we do not look at each end in outer loop
Q += distmx[u, v]
end
end
kout[c1] += distmx[u, v]
kin[c2] += distmx[u, v]
if u == v && !is_directed(g)
#Since we do not look at each end in outer loop
kout[c1] += distmx[u, v]
kin[c2] += distmx[u, v]
end
end
end
Q = Q * m
@inbounds for i in 1:nc
Q -= γ * kin[i] * kout[i]
end
return Q / m^2
Q = Q/m - γ * sum(kin .* kout) / m^2
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@Krastanov Krastanov Feb 19, 2026

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I imagine whoever wrote the previous inbounds loop wanted to avoid the allocation. The new format creates a temporary array to store kin .* kout before performing the sum. I guess I personally do not care about this type of micro-optimization and prefer your new code, but mentioning this here for documentation purposes.

julia> f(a,b) = sum(a .* b)
f (generic function with 1 method)

julia> g(a,b) = begin s = 0.0; for i in eachindex(a); s += a[i]*b[i]; end; s end

g (generic function with 1 method)

julia> a = rand(100);

julia> b = rand(100);

julia> @time f(a, b);
  0.027304 seconds (13.31 k allocations: 711.914 KiB, 99.97% compilation time)

julia> @time f(a, b);
  0.000004 seconds (3 allocations: 944 bytes)

julia> @time g(a, b);
  0.007733 seconds (5.17 k allocations: 261.992 KiB, 99.91% compilation time)

julia> @time g(a, b);
  0.000003 seconds (1 allocation: 16 bytes)

and here are a few more options to consider for simpler syntax

julia> h(a,b) = sum(zip(a,b)) do (x,y); x*y; end
h (generic function with 1 method)

julia> k(a,b) = sum(splat(*), (a,b))
k (generic function with 1 method)

julia> @time h(a, b);
  0.018568 seconds (131.63 k allocations: 6.261 MiB, 15.16% gc time, 99.96% compilation time)

julia> @time h(a, b);
  0.000002 seconds (1 allocation: 16 bytes)

julia> @time k(a, b);
  0.028699 seconds (146.38 k allocations: 7.347 MiB, 99.72% compilation time)

julia> @time k(a, b);
  0.000016 seconds (403 allocations: 7.891 KiB)

return Q
end
41 changes: 38 additions & 3 deletions test/community/modularity.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,13 @@
@test isapprox(Q2, 0.6071428571428571, atol=1e-3)
end

# Test with self loop
add_edge!(barbell, 1, 1)
for g in test_generic_graphs(barbell)
Q = @inferred(modularity(g, c))
@test isapprox(Q, 0.3671875, atol=1e-3)
end

# 2. directed test cases
triangle = SimpleDiGraph(3)
add_edge!(triangle, 1, 2)
Expand Down Expand Up @@ -65,6 +72,20 @@
Q = @inferred(modularity(g, c, distmx=d))
@test isapprox(Q, 0.045454545454545456, atol=1e-3)
end
# Add self loop with weight 5
add_edge!(barbell, 1, 1)
d = [
[5 1 1 0 0 0]
[1 0 1 0 0 0]
[1 1 0 1 0 0]
[0 0 1 0 1 1]
[0 0 0 1 0 1]
[0 0 0 1 1 0]
]
for g in test_generic_graphs(barbell)
Q = @inferred(modularity(g, c, distmx=d))
@test isapprox(Q, 0.329861, atol=1e-3)
end

# 3.2. directed and weighted test cases
triangle = SimpleDiGraph(3)
Expand All @@ -73,12 +94,12 @@
add_edge!(triangle, 3, 1)

barbell = blockdiag(triangle, triangle)
add_edge!(barbell, 1, 4) # this edge has a weight of 5
add_edge!(barbell, 3, 4) # this edge has a weight of 5
c = [1, 1, 1, 2, 2, 2]
d = [
[0 1 0 5 0 0]
[0 1 0 0 0 0]
[0 0 1 0 0 0]
[1 0 0 0 0 0]
[1 0 0 5 0 0]
[0 0 0 0 1 0]
[0 0 0 0 0 1]
[0 0 0 1 0 0]
Expand All @@ -87,4 +108,18 @@
Q = @inferred(modularity(g, c, distmx=d))
@test isapprox(Q, 0.1487603305785124, atol=1e-3)
end
# Add self loop with weight 5
add_edge!(barbell, 1, 1)
d = [
[5 1 0 0 0 0]
[0 0 1 0 0 0]
[1 0 0 1 0 0]
[0 0 0 0 1 0]
[0 0 0 0 0 1]
[0 0 0 1 0 0]
]
for g in test_generic_graphs(barbell)
Q = @inferred(modularity(g, c, distmx=d))
@test isapprox(Q, 0.333333333, atol=1e-3)
end
end
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