App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Temporal link prediction in dynamic networks combining structural features with multi-layer gated fusion

Su Zhan
Shao Jie
Ai Jun
Zhao Weicheng
School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0156

Abstract

To address the insufficient utilization of node structural roles, redundant information introduced by multi-layer temporal representation fusion, and the loss of shallow local interaction patterns and intermediate-layer temporal semantics when only the final-layer representation is retained, which may further limit model adaptability to new nodes and sparse interaction scenarios, this study proposed a temporal link prediction method named Structure-enhanced Hierarchical Gated TPNet (SHG-TPNet) . SHG-TPNet used the Temporal Walk Matrix Projection Network (TPNet) as its backbone. It first constructed structural features, including degree centrality, community size, and intra-community neighbor ratio, from historical interactions visible during training to enhance the representation of local topological roles of nodes. It then employed per-layer embeddings to preserve temporal semantics at different depths and finally introduced a gated fusion mechanism to adaptively select representations from different layers for link prediction. Experiments on seven real-world dynamic network datasets, including Wikipedia, Reddit, and SocialEvo, show that, compared with TPNet, SHG-TPNet improves Test AP and Test AUC by 0.0167 and 0.0234 on average in the standard test setting, and improves New AP and New AUC by 0.0088 and 0.0108 on average in the new-node prediction setting. Ablation experiments verify the effectiveness of the three enhancement modules.

Foundation Support

国家自然科学基金资助项目(61803264)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0156
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-21] Accepted Paper

Cite This Article

苏湛, 邵杰, 艾均, 等. 结合结构特征与多层门控融合的动态网络时序链路预测方法 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0156. (Su Zhan, Shao Jie, Ai Jun, et al. Temporal link prediction in dynamic networks combining structural features with multi-layer gated fusion [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0156. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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