Anchor Link Prediction in Online Social Network Using Graph Embedding and Binary Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246987" target="_blank" >RIV/61989100:27240/20:10246987 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-63007-2_18" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-63007-2_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63007-2_18" target="_blank" >10.1007/978-3-030-63007-2_18</a>

Result language

angličtina

Original language name

Anchor Link Prediction in Online Social Network Using Graph Embedding and Binary Classification

Original language description

With the widespread popularity as well as the variety of different online social networks. Today, each user can join many social networks at the same time for many different purposes. They can join Facebook to share and update status, join Instagram to share photos, join LinkedIn to share in work, etc. As the scale and number of online social networks grows, social network analysis has become a widespread problem in many scientific disciplines. One of the emerging topics in social network analysis is anchor link prediction problem which identifies the same user across different networks. In this paper, we propose an algorithm to predict the missing anchor links between users across source and target network. Our algorithm represents the vertices and the edges in source and target network as the represenation vectors, we then apply the binary classification algorithms to predict the matching score of all pairs of vertices between the source and target network. The experimental results show that our algorithm performs better traditional anchor link prediction algorithms. (C) 2020, Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12496

ISBN

978-3-030-63006-5

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

12

Pages from-to

229-240

Publisher name

Springer

Place of publication

Cham

Event location

Danang

Event date

Nov 30, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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