Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques

• Published in: Information sciences Vol. 645; p. 119331
• Main Authors: Le, Van-Vang, Pham, Phu, Snasel, Vaclav, Yun, Unil, Vo, Bay
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2023
• Subjects: Anchor link prediction; Graph neural network; Network alignment; Network representation learning; Network representation learning; Network alignment; Anchor link prediction; Graph neural network
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2023.119331

There are multiple types of information networks, including: social networks, citation networks, email communications networks, etc. are becoming popular in recent years. They have attracted a lot of researchers in multiple disciplines. Within this research domain, network alignment is considered as one of active topic due to its potential applications in real-world systems. In fact, with the popularity and diversity of information network types, most of users may participate in multiple information networks for many purposes. Thank to tremendous grow in the number of information networks recently, data analysis in information network has become a major challenge in multiple scientific disciplines. Anchor link prediction, also known as network alignment, which is aimed to match the users between different networks who have the same identification, is one of the emerging research directions in this domain. However, recent anchor link prediction baselines still lack the capability of sufficiently preserving global graph-structured features of individual information networks to obtain better prediction results. To overcome this challenge, we propose a novel model to align users between information networks using a seed set of known anchor links. To achieve better representations of individual networks in our proposed model, four embedding techniques are combined and learnt from the same latent space. We then apply the aggregation method to achieve a final network aligned embedding matrix which is later utilized to deal with the anchor link prediction problem. We run comprehensive experiments in real-life network alignment datasets to evaluate the effectiveness and compare them with the up-to-date baseline methods.