SEGNN4SLP: Structure Enhanced Graph Neural Networks for Service Link Prediction

Figures & Tables

input: target edge (i,j); input graph G; node characterizes X
output: forecast score s,
1 /* extracts enclosing subgraph */	2 G ← G
3 zu ← Structural Encoding (G_s, i, j), ∀_u ∈ G_s;	4 S_structure ← MLP (Z_i ° z_j)
5/* feature fusion */cu←{ pi,j,Gs },∀u∈Gs;xu∼(0)←xu(0),∀u∈Gs;x˜u←MLP(∼(0)xu),∀u∈Gs;hu(0)←x˜u,∀u∈Gs;
6 /* GNN message passing /for k=1,2…K do: for u ∈ G do: h_u^(k) ← Equation* 4; endend
7 h_G ← SortPool(h_u^(k)\| u ∈ G_s,k = 1,…,K);	8 S_semantic ← MLP(h_G)
9 S = S_structure + S_semantic

input: target nodes v_i, v_j; enclosing subgraph G_s
output: node embedding z
1 /extracts the routes/	2 p_i,j (G;i,j)
3 /* generate node structural features */	4 c_u ← {p_i.j,G_S}, ∀_u ∈ G_S;
5 Zu(0)← one −hot(min(cu,λ)),∀u∈Gs;	6 /* encode with a GCN layer and a MLP */
7 for u ∈ G_S do	8 Zu(I)←AGGREGATE(Zv(0),∀u∈ N(u));
9 end for	10 Z_u → MLP(z_u), ∀_u ∈ G_s;