Link Prediction on Complex Networks: An Experimental Survey

• Published in: Data Science and Engineering Vol. 7; no. 3; pp. 253 - 278
• Main Authors: Wu, Haixia, Song, Chunyao, Ge, Yao, Ge, Tingjian
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2022; Springer; Springer Nature B.V
• Subjects: Air transportation; Algorithm Analysis and Problem Complexity; Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Database Management; Epidemics; Experiments; Graph neural networks; Graph representations; International trade; Network analysis; Neural networks; Physics; Proteins; Review/Survey Papers; Social networks; Statistics for Engineering; Surveys; Systems and Data Security; Taxonomy; Topology; Link prediction; 99-00; 00-01; Data mining; Network analysis; Complex networks
• ISSN: 2364-1185; 2364-1541; 2364-1541
• DOI: 10.1007/s41019-022-00188-2

Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.