Network Alignment across Social Networks Using Multiple Embedding Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251904" target="_blank" >RIV/61989100:27240/22:10251904 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-7390/10/21/3972" target="_blank" >https://www.mdpi.com/2227-7390/10/21/3972</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math10213972" target="_blank" >10.3390/math10213972</a>

Result language

angličtina

Original language name

Network Alignment across Social Networks Using Multiple Embedding Techniques

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

2227-7390

Volume of the periodical

10

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

nestrankovano

UT code for WoS article

000881271100001

EID of the result in the Scopus database

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