Network Alignment across Social Networks Using Multiple Embedding Techniques

Le Van-Vang, Toai Kim Tran (), Bich-Ngan T. Nguyen, Quoc-Dung Nguyen and Vaclav Snasel
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Le Van-Vang: Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Toai Kim Tran: Faculty of Economics, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, Vietnam
Bich-Ngan T. Nguyen: Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 708 33 Ostrava, Czech Republic
Quoc-Dung Nguyen: Faculty of Mechanical-Electrical and Computer Engineering, School of Engineering and Technology, Van Lang University, Ho Chi Minh City 700000, Vietnam
Vaclav Snasel: Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 708 33 Ostrava, Czech Republic

Mathematics, 2022, vol. 10, issue 21, 1-18

Abstract: Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.

Keywords: user identity linkage; network alignment; graph embedding; graph neural network; graph attention network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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