文献类型：期刊文献

英文题名：Research on a Link Prediction Algorithm Based on Hypergraph Representation Learning

作者：Fu, Kang[1];Yan, Guanghui[1,2];Luo, Hao[1,3];Chang, Wenwen[1,2];Li, Jingwen[1,2]

第一作者：Fu, Kang

通信作者：Yan, GH[1];Yan, GH[2]

机构：[1]Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]Key Lab Media Convergence Technol & Commun Gansu P, Lanzhou 730000, Peoples R China;[3]Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730013, Peoples R China

第一机构：Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China

通信机构：[1]corresponding author), Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]corresponding author), Key Lab Media Convergence Technol & Commun Gansu P, Lanzhou 730000, Peoples R China.

年份：2023

卷号：12

期号：23

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85179312991);WOS:【SCI-EXPANDED(收录号:WOS:001116649100001)】；

基金：Thanks to the reviewers and editors for their valuable comments and suggestions.

语种：英文

外文关键词：link prediction; complex network; Clique; hypermotifs; random walk

摘要：Link prediction is a crucial area of study within complex networks research. Mapping nodes to low-dimensional vectors through network embeddings is a vital technique for link prediction. Most of the existing methods employ "node-edge"-structured networks to model the data and learn node embeddings. In this paper, we initially introduce the Clique structure to enhance the existing model and investigate the impact of introducing two Clique structures (LECON: Learning Embedding based on Clique Of the Network) and nine motifs (LEMON: Learning Embedding based on Motif Of the Network), respectively, on experimental performance. Subsequently, we introduce a hypergraph to model the network and reconfigure the network by mapping hypermotifs to two structures: open hypermotif and closed hypermotif, respectively. Then, we introduce hypermotifs as supernodes to capture the structural similarity between nodes in the network (HMRLH: HyperMotif Representation Learning on Hypergraph). After that, taking into account the connectivity and structural similarity of the involved nodes, we propose the Depth and Breadth Motif Random Walk method to acquire node sequences. We then apply this method to the LEMON (LEMON-DB: LEMON-Depth and Breadth Motif Random Walk) and HMRLH (HMRLH-DB: HMRLH-Depth and Breadth Motif Random Walk) algorithms. The experimental results on four different datasets indicate that, compared with the LEMON method, the LECON method improves experimental performance while reducing time complexity. The HMRLH method, utilizing hypernetwork modeling, proves more effective in extracting node features. The LEMON-DB and HMRLH-DB methods, incorporating new random walk approaches, outperform the original methods in the field of link prediction. Compared with state-of-the-art baselines, the proposed approach in this paper effectively enhances link prediction accuracy, demonstrating a certain level of superiority.