Self-Driven Graph Volterra Models for Higher-Order Link Prediction

Mario Coutino; Georgios V. Karanikolas; Geert Leus; Georgios B. Giannakis

doi:10.1109/ICASSP40776.2020.9053655

Self-Driven Graph Volterra Models for Higher-Order Link Prediction

Mario Coutino, Georgios V. Karanikolas, Geert Leus, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict such higher-order interactions, but no principled method has been put forth to tackle this challenge so far. Cross-fertilizing ideas from Volterra series and linear structural equation models, the present paper introduces self-driven graph Volterra models that can capture higher-order interactions among nodal observables available in networked data. The novel model is validated for the higher-order link prediction task using real interaction data from social networks.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3887-3891
Number of pages	5
ISBN (Electronic)	9781509066315
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9053655
State	Published - May 2020
Event	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: May 4 2020 → May 8 2020

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2020-May
ISSN (Print)	1520-6149

Conference

Conference	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/Territory	Spain
City	Barcelona
Period	5/4/20 → 5/8/20

Bibliographical note

Funding Information:
Acknowledgements. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO); and M. Coutino in part by CONACYT. G. V. Karanikolas and G. B. Giannakis were supported in part by NSF grants 1711471 and 1901134. Emails: {m.a.coutinominguez, g.j.t.leus}@tudelft.nl; {karan029,georgios}@umn.edu

Publisher Copyright:
© 2020 IEEE.

Keywords

Volterra series
higher-order interactions
link prediction
network data models
structural equation models

Access

10.1109/ICASSP40776.2020.9053655

OpenUrl availability

Full text

Cite this

Coutino, M., Karanikolas, G. V., Leus, G., & Giannakis, G. B. (2020). Self-Driven Graph Volterra Models for Higher-Order Link Prediction. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 3887-3891). Article 9053655 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9053655

Self-Driven Graph Volterra Models for Higher-Order Link Prediction. / Coutino, Mario; Karanikolas, Georgios V.; Leus, Geert et al.
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 3887-3891 9053655 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Coutino, M, Karanikolas, GV, Leus, G & Giannakis, GB 2020, Self-Driven Graph Volterra Models for Higher-Order Link Prediction. in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9053655, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, Institute of Electrical and Electronics Engineers Inc., pp. 3887-3891, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spain, 5/4/20. https://doi.org/10.1109/ICASSP40776.2020.9053655

Coutino M, Karanikolas GV, Leus G, Giannakis GB. Self-Driven Graph Volterra Models for Higher-Order Link Prediction. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 3887-3891. 9053655. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP40776.2020.9053655

Coutino, Mario ; Karanikolas, Georgios V. ; Leus, Geert et al. / Self-Driven Graph Volterra Models for Higher-Order Link Prediction. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 3887-3891 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{68a859fe450e4a5fbcd216677e1857aa,

title = "Self-Driven Graph Volterra Models for Higher-Order Link Prediction",

abstract = "Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict such higher-order interactions, but no principled method has been put forth to tackle this challenge so far. Cross-fertilizing ideas from Volterra series and linear structural equation models, the present paper introduces self-driven graph Volterra models that can capture higher-order interactions among nodal observables available in networked data. The novel model is validated for the higher-order link prediction task using real interaction data from social networks.",

keywords = "Volterra series, higher-order interactions, link prediction, network data models, structural equation models",

author = "Mario Coutino and Karanikolas, {Georgios V.} and Geert Leus and Giannakis, {Georgios B.}",

note = "Funding Information: Acknowledgements. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO); and M. Coutino in part by CONACYT. G. V. Karanikolas and G. B. Giannakis were supported in part by NSF grants 1711471 and 1901134. Emails: {m.a.coutinominguez, g.j.t.leus}@tudelft.nl; {karan029,georgios}@umn.edu Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

year = "2020",

month = may,

doi = "10.1109/ICASSP40776.2020.9053655",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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

pages = "3887--3891",

booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",

}

TY - GEN

T1 - Self-Driven Graph Volterra Models for Higher-Order Link Prediction

AU - Coutino, Mario

AU - Karanikolas, Georgios V.

AU - Leus, Geert

AU - Giannakis, Georgios B.

N1 - Funding Information: Acknowledgements. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO); and M. Coutino in part by CONACYT. G. V. Karanikolas and G. B. Giannakis were supported in part by NSF grants 1711471 and 1901134. Emails: {m.a.coutinominguez, g.j.t.leus}@tudelft.nl; {karan029,georgios}@umn.edu Publisher Copyright: © 2020 IEEE.

PY - 2020/5

Y1 - 2020/5

N2 - Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict such higher-order interactions, but no principled method has been put forth to tackle this challenge so far. Cross-fertilizing ideas from Volterra series and linear structural equation models, the present paper introduces self-driven graph Volterra models that can capture higher-order interactions among nodal observables available in networked data. The novel model is validated for the higher-order link prediction task using real interaction data from social networks.

AB - Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict such higher-order interactions, but no principled method has been put forth to tackle this challenge so far. Cross-fertilizing ideas from Volterra series and linear structural equation models, the present paper introduces self-driven graph Volterra models that can capture higher-order interactions among nodal observables available in networked data. The novel model is validated for the higher-order link prediction task using real interaction data from social networks.

KW - Volterra series

KW - higher-order interactions

KW - link prediction

KW - network data models

KW - structural equation models

UR - http://www.scopus.com/inward/record.url?scp=85089220422&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089220422&partnerID=8YFLogxK

U2 - 10.1109/ICASSP40776.2020.9053655

DO - 10.1109/ICASSP40776.2020.9053655

M3 - Conference contribution

AN - SCOPUS:85089220422

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 3887

EP - 3891

BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020

Y2 - 4 May 2020 through 8 May 2020

ER -

Self-Driven Graph Volterra Models for Higher-Order Link Prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this