Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253781" target="_blank" >RIV/61989100:27240/23:10253781 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001063574700001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001063574700001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2023.119331" target="_blank" >10.1016/j.ins.2023.119331</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques
Popis výsledku v původním jazyce
There are multiple types of information networks, including: social networks, citation networks, email communications networks, etc. are becoming popular in recent years. They have attracted a lot of researchers in multiple disciplines. Within this research domain, network alignment is considered as one of active topic due to its potential applications in real-world systems. In fact, with the popularity and diversity of information network types, most of users may participate in multiple information networks for many purposes. Thank to tremendous grow in the number of information networks recently, data analysis in information network has become a major challenge in multiple scientific disciplines. Anchor link prediction, also known as network alignment, which is aimed to match the users between different networks who have the same identification, is one of the emerging research directions in this domain. However, recent anchor link prediction baselines still lack the capability of sufficiently preserving global graph-structured features of individual information networks to obtain better prediction results. To overcome this challenge, we propose a novel model to align users between information networks using a seed set of known anchor links. To achieve better representations of individual networks in our proposed model, four embedding techniques are combined and learnt from the same latent space. We then apply the aggregation method to achieve a final network aligned embedding matrix which is later utilized to deal with the anchor link prediction problem. We run comprehensive experiments in real-life network alignment datasets to evaluate the effectiveness and compare them with the up-to-date baseline methods. (C) 2023
Název v anglickém jazyce
Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques
Popis výsledku anglicky
There are multiple types of information networks, including: social networks, citation networks, email communications networks, etc. are becoming popular in recent years. They have attracted a lot of researchers in multiple disciplines. Within this research domain, network alignment is considered as one of active topic due to its potential applications in real-world systems. In fact, with the popularity and diversity of information network types, most of users may participate in multiple information networks for many purposes. Thank to tremendous grow in the number of information networks recently, data analysis in information network has become a major challenge in multiple scientific disciplines. Anchor link prediction, also known as network alignment, which is aimed to match the users between different networks who have the same identification, is one of the emerging research directions in this domain. However, recent anchor link prediction baselines still lack the capability of sufficiently preserving global graph-structured features of individual information networks to obtain better prediction results. To overcome this challenge, we propose a novel model to align users between information networks using a seed set of known anchor links. To achieve better representations of individual networks in our proposed model, four embedding techniques are combined and learnt from the same latent space. We then apply the aggregation method to achieve a final network aligned embedding matrix which is later utilized to deal with the anchor link prediction problem. We run comprehensive experiments in real-life network alignment datasets to evaluate the effectiveness and compare them with the up-to-date baseline methods. (C) 2023
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10200 - Computer and information sciences
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Information sciences
ISSN
0020-0255
e-ISSN
1872-6291
Svazek periodika
645
Číslo periodika v rámci svazku
říjen 2023
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
14
Strana od-do
—
Kód UT WoS článku
001063574700001
EID výsledku v databázi Scopus
2-s2.0-85162883482