fix

GNNGraphs/src/transform.jl

@@ -149,57 +149,6 @@ function remove_self_loops(g::GNNGraph{<:ADJMAT_T})
              g.ndata, g.edata, g.gdata)
 end
 
-"""
-    remove_edges(g::GNNGraph, edges_to_remove::AbstractVector{<:Integer})
-
-Remove specified edges from a GNNGraph.
-
-# Arguments
-- `g`: The input graph from which edges will be removed.
-- `edges_to_remove`: Vector of edge indices to be removed.
-
-# Returns
-A new GNNGraph with the specified edges removed.
-
-# Example
-```julia
-julia> using GraphNeuralNetworks
-
-# Construct a GNNGraph
-julia> g = GNNGraph([1, 1, 2, 2, 3], [2, 3, 1, 3, 1])
-GNNGraph:
-  num_nodes: 3
-  num_edges: 5
-  
-# Remove the second edge
-julia> g_new = remove_edges(g, [2]);
-
-julia> g_new
-GNNGraph:
-  num_nodes: 3
-  num_edges: 4
-```
-"""
-function remove_edges(g::GNNGraph{<:COO_T}, edges_to_remove::AbstractVector{<:Integer})
-    s, t = edge_index(g)
-    w = get_edge_weight(g)
-    edata = g.edata
-
-    mask_to_keep = trues(length(s))
-
-    mask_to_keep[edges_to_remove] .= false
-
-    s = s[mask_to_keep]
-    t = t[mask_to_keep]
-    edata = getobs(edata, mask_to_keep)
-    w = isnothing(w) ? nothing : getobs(w, mask_to_keep)
-
-    return GNNGraph((s, t, w),
-             g.num_nodes, length(s), g.num_graphs,
-             g.graph_indicator,
-             g.ndata, edata, g.gdata)
-end
-
 """
     remove_edges(g::GNNGraph, edges_to_remove::AbstractVector{<:Integer})
     remove_edges(g::GNNGraph, p::Float64=0.5)
@@ -275,6 +224,45 @@ function remove_edges(g::GNNGraph{<:COO_T}, p = 0.5)
              g.ndata, edata, g.gdata)
 end
 
+"""
+    remove_multi_edges(g::GNNGraph; aggr=+)
+
+Remove multiple edges (also called parallel edges or repeated edges) from graph `g`.
+Possible edge features are aggregated according to `aggr`, that can take value 
+`+`,`min`, `max` or `mean`.
+
+See also [`remove_self_loops`](@ref), [`has_multi_edges`](@ref), and [`to_bidirected`](@ref).
+"""
+function remove_multi_edges(g::GNNGraph{<:COO_T}; aggr = +)
+    s, t = edge_index(g)
+    w = get_edge_weight(g)
+    edata = g.edata
+    num_edges = g.num_edges
+    idxs, idxmax = edge_encoding(s, t, g.num_nodes)
+
+    perm = sortperm(idxs)
+    idxs = idxs[perm]
+    s, t = s[perm], t[perm]
+    edata = getobs(edata, perm)
+    w = isnothing(w) ? nothing : getobs(w, perm)
+    idxs = [-1; idxs]
+    mask = idxs[2:end] .> idxs[1:(end - 1)]
+    if !all(mask)
+        s, t = s[mask], t[mask]
+        idxs = similar(s, num_edges)
+        idxs .= 1:num_edges
+        idxs .= idxs .- cumsum(.!mask)
+        num_edges = length(s)
+        w = _scatter(aggr, w, idxs, num_edges)
+        edata = _scatter(aggr, edata, idxs, num_edges)
+    end
+
+    return GNNGraph((s, t, w),
+             g.num_nodes, num_edges, g.num_graphs,
+             g.graph_indicator,
+             g.ndata, edata, g.gdata)
+end
+
 """
     remove_nodes(g::GNNGraph, nodes_to_remove::AbstractVector)