Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders

Reyhan Kevser Keser; Indrit Nallbani; Nurullah Calik; Aydin Ayanzadeh; Behcet Ugur Tgreyin

doi:10.1109/SIU49456.2020.9302271

Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders

Reyhan Kevser Keser
, Indrit Nallbani
, Nurullah Calik
, Aydin Ayanzadeh
, Behcet Ugur Tgreyin

Bilişim Enstitüsü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

6 Atıf (Scopus)

Özet

Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728172064
DOI'lar	https://doi.org/10.1109/SIU49456.2020.9302271
Yayın durumu	Yayınlandı - 5 Eki 2020
Etkinlik	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Türkiye Süre: 5 Eki 2020 → 7 Eki 2020

Yayın serisi

Adı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/Bölge	Türkiye
Şehir	Gaziantep
Periyot	5/10/20 → 7/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Finansman

This work is supported in part by Istanbul Technical University (ITU) Vodafone Future Lab under Project No. ITUVF20180901P04.

Finansörler	Finansör numarası
İTÜ Vodafone Future Lab	ITUVF20180901P04
Istanbul Teknik Üniversitesi

Belgeye Erişim

10.1109/SIU49456.2020.9302271

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Keser, R. K., Nallbani, I., Calik, N., Ayanzadeh, A., & Tgreyin, B. U. (2020). Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings Makale 9302271 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU49456.2020.9302271

Keser, Reyhan Kevser ; Nallbani, Indrit ; Calik, Nurullah et al. / Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders. 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

@inproceedings{8925e44f18194038ac801018ab0b67f5,

title = "Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders",

abstract = "Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).",

keywords = "Graph Embedding, Residual Learning, Variational Graph Autoen-coders",

author = "Keser, \{Reyhan Kevser\} and Indrit Nallbani and Nurullah Calik and Aydin Ayanzadeh and Tgreyin, \{Behcet Ugur\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 28th Signal Processing and Communications Applications Conference, SIU 2020 ; Conference date: 05-10-2020 Through 07-10-2020",

year = "2020",

month = oct,

day = "5",

doi = "10.1109/SIU49456.2020.9302271",

language = "English",

series = "2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings",

address = "United States",

}

Keser, RK, Nallbani, I, Calik, N, Ayanzadeh, A & Tgreyin, BU 2020, Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders. in 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings., 9302271, 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Türkiye, 5/10/20. https://doi.org/10.1109/SIU49456.2020.9302271

Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders. / Keser, Reyhan Kevser; Nallbani, Indrit; Calik, Nurullah et al.
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9302271 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders

AU - Keser, Reyhan Kevser

AU - Nallbani, Indrit

AU - Calik, Nurullah

AU - Ayanzadeh, Aydin

AU - Tgreyin, Behcet Ugur

PY - 2020/10/5

Y1 - 2020/10/5

N2 - Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).

AB - Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).

KW - Graph Embedding

KW - Residual Learning

KW - Variational Graph Autoen-coders

UR - https://www.scopus.com/pages/publications/85100302526

U2 - 10.1109/SIU49456.2020.9302271

DO - 10.1109/SIU49456.2020.9302271

M3 - Conference contribution

AN - SCOPUS:85100302526

T3 - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

BT - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th Signal Processing and Communications Applications Conference, SIU 2020

Y2 - 5 October 2020 through 7 October 2020

ER -

Keser RK, Nallbani I, Calik N, Ayanzadeh A, Tgreyin BU. Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9302271. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). doi: 10.1109/SIU49456.2020.9302271

Graph Embedding for Link Prediction Using Residual Variational Graph Autoencoders

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap