DeepLink: A novel link prediction framework based on deep learning. (October 2021)

• Record Type: Journal Article
• Authors: Keikha, Mohammad Mehdi; Rahgozar, Maseud; Asadpour, Masoud
• Is Part Of: Journal of information science. Volume 47:Number 5(2021)
• Page Start: 642
• Page End: 657
• Publication Date: 2021-10
• Subjects: Deep learning -- link prediction -- network embedding -- social networks -- unsupervised feature learning; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551519891345
• Languages: English
• ISSNs: 0165-5515

Abstract

Recently, link prediction has attracted more attention from various disciplines such as computer science, bioinformatics and economics. In link prediction, numerous information such as network topology, profile information and user-generated contents are considered to discover missing links between nodes. Whereas numerous previous researches had focused on the structural features of the networks for link prediction, recent studies have shown more interest in profile and content information, too. So, some of these researches combine structural and content information. However, some issues such as scalability and feature engineering need to be investigated to solve a few remaining problems. Moreover, most of the previous researches are presented only for undirected and unweighted networks. In this article, a novel link prediction framework named 'DeepLink' is presented, which is based on deep learning techniques. While deep learning has the advantage of extracting automatically the best features for link prediction, many other link prediction algorithms need manual feature engineering. Moreover, in the proposed framework, both structural and content information are employed. The framework is capable of using different structural feature vectors that are prepared by various link prediction methods. It learns all proximity orders that are presented on a network during the structural feature learning. We have evaluated the effectiveness of DeepLink on two real social network datasets, Telegram and irBlogs. On both datasets, the proposed framework outperforms several other structural and hybrid approaches for link prediction.