An ensemble model for link prediction based on graph embedding

• Published in: Decision Support Systems 2022-06, Vol.157, p.113753, Article 113753
• Main Authors: Chen, Yen-Liang, Hsiao, Chen-Hsin, Wu, Chia-Chi
• Format: Article
• Language: English
• Subjects: Bioinformatics; Classifiers; Embedding; Ensemble learning; Graph embedding; Link prediction; Mathematical analysis; Network analysis; Nodes; Performance enhancement; Performance evaluation; Prediction models; Random walk; Representations; Social networks
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2022.113753

A network is a form of data representation and is widely used in many fields. For example, in social networks, we regard nodes as individuals or groups, and the edges between nodes are called links, that is, the interaction between people. By analyzing the interaction of nodes, we can learn more about network relationships. The core idea of link prediction is to predict whether there is a new relationship between a pair of nodes or to discover hidden links in the network. Link prediction has been applied to many fields such as social networking, e-commerce, bioinformatics, and so on. In addition, many studies have used graph embedding for link prediction, which effectively preserves the network structure and converts node information into a low-dimensional vector space. In this research, we used three graph embedding approaches: matrix decomposition based methods, random walk based methods, and deep learning based methods. Since each method has its own advantages and disadvantages, we propose an ensemble model to combine these graph embeddings into a new representation of each node. Then, we designed a two-stage link prediction model based on a multi-classifier ensemble and took the new node representation as its input. Performance evaluation was conducted on multiple data sets. Experimental results show that the integration of multiple embedding methods and multiple classifiers can significantly improve the performance of link prediction. •Different graph embedding methods are integrated for link prediction.•Stacking ensemble structure is used to improve model performance.•Experimental results show that the proposed model significantly improves the accuracy of link prediction.