Supervised Classification for Link Prediction in Facebook Ego Networks With Anonymized Profile Information

Giubilei, Rossella; Brutti, Pierpaolo

doi:10.1007/s00357-021-09408-2

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Due to the explosion in the number of online social networks, as well as the enormous benefits that they bring, social networks have recently become ubiquitous in our daily social lives. There is much research that has explored hidden information in such complex social networks, such as network representation learning [1][2][3], community detection [4,5], node classification [6,7], recommendation systems [8,9], link prediction [1,10,11], etc., with the diversity and extensive popularity of various social networks, such as Facebook, Instagram, Twitter, etc. Currently, each user can simultaneously join multiple social networks for many different purposes.…”

Section: Introductionmentioning

confidence: 99%

Network Alignment across Social Networks Using Multiple Embedding Techniques

Toai

Nguyen

et al. 2022

Mathematics

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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.

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