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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

12

Strana od-do

229-240

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Danang

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—