SEGNN4SLP: Structure Enhanced Graph Neural Networks for Service Link Prediction

Yuxi Lin; Mengfei1 Li; Nuo Chen

doi:10.2478/ijanmc-2024-0032

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SEGNN4SLP: Structure Enhanced Graph Neural Networks for Service Link Prediction

International Journal of Advanced Network, Monitoring and Controls

Volume 9 (2024): Issue 4 (December 2024)

By: Yuxi Lin, Mengfei1 Li and Nuo Chen

Open Access

|Dec 2024

Figures & Tables

The SEGNN4SLP framework demonstrates the use of route labeling (PL) on a 1-hop subgraph that includes nodes a and b. In (a), we notice a route with a length of 2, in (b) a route with a length of 3, and in (c) a route with a length of 4. Every unique pathway is depicted using a distinct hue. The nodes are labeled and the labels are presented above the nodes. Nodes with different labels are shown in distinct colors.

Assign different coefficients between nodes by GAT.

j_jhp-2024-0019_utab_002

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

j_jhp-2024-0019_utab_001

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;

Comparison of different methods in nDCG@k_

	K=5	K=10	K=15	K=20	K=25
Node2vec	0.2314	0.2786	0.3278	0.3529	0.3604
GCN	0.2811	0.3378	0.3588	0.3687	0.3786
GraphSAGE	0.2823	0.3468	0.3770	0.3819	0.3793
GAT	0.2811	0.3398	0.3764	0.3987	0.3859
SEAL	0.2994	0.3410	0.3896	0.4055	0.3986
SEGNN4SLP	0.3516	0.3814	0.4156	0.4258	0.4288

Results for SEGNN4SLP, SEGNN4SLP-1, SEGNN4SLP-2_

Methods	Recall		nDCG
Methods	Recall@5	Recall@25	nDCG@5	nDCG@25
SEGNN4SLP-1	0.3389	0.4844	0.3486	0.3855
SEGNN4SLP-2	0.3284	0.4964	0.3357	0.3746
SEGNN4SLP	0.3598	0.5287	0.3617	0.4137

Comparison of different methods in Recall@k_

	K=5	K=10	K=15	K=20	K=25
Node2vec	0.2185	0.2915	0.3473	0.3761	0.4012
GCN	0.2729	0.3461	0.3684	0.4561	0.4716
GraphSAGE	0.2816	0.3553	0.3941	0.4611	0.4933
GAT	0.2810	0.3513	0.3902	0.4687	0.4910
SEAL	0.2984	0.3588	0.4013	0.4701	0.4987
SEGNN4SLP	0.3514	0.3981	0.4586	0.4981	0.5231

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DOI: https://doi.org/10.2478/ijanmc-2024-0032 | Journal eISSN: 2470-8038

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Language: English

Page range: 9 - 18

Published on: Dec 31, 2024

Published by: Xi’an Technological University

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Network Representation,

Web Service,

Mobile Network,

Graph Attention network,

Link Prediction

Related subjects:

Computer sciences,

Computer sciences, other

© 2024 Yuxi Lin, Mengfei1 Li, Nuo Chen, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 9 (2024): Issue 4 (December 2024)

SEGNN4SLP: Structure Enhanced Graph Neural Networks for Service Link Prediction

Figures & Tables

Figure 1

Figure 2

Figure 3

Figure 4

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Figure 7

j_jhp-2024-0019_utab_002

j_jhp-2024-0019_utab_001

Comparison of different methods in nDCG@k_

Results for SEGNN4SLP, SEGNN4SLP-1, SEGNN4SLP-2_

Comparison of different methods in Recall@k_

Paradigm

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