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

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

22 Downloads (Pure)

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
Title of host publication	ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	3887-3891
Number of pages	5
ISBN (Electronic)	978-1-5090-6631-5
ISBN (Print)	978-1-5090-6632-2
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9053655
Publication status	Published - 2020
Event	ICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020

Conference

Conference	ICASSP 2020
Country/Territory	Spain
City	Barcelona
Period	4/05/20 → 8/05/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

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

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10.1109/ICASSP40776.2020.9053655

Self-Driven_Graph_Volterra_Models_for_Higher-Order_Link_PredictionFinal published version, 333 KB

Cite this

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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 = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; ICASSP 2020 : IEEE International Conference on Acoustics, Speech and Signal Processing ; Conference date: 04-05-2020 Through 08-05-2020",

year = "2020",

doi = "10.1109/ICASSP40776.2020.9053655",

language = "English",

isbn = "978-1-5090-6632-2",

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

publisher = "IEEE",

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

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2020

Y1 - 2020

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

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M3 - Conference contribution

SN - 978-1-5090-6632-2

SP - 3887

EP - 3891

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

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T2 - ICASSP 2020

Y2 - 4 May 2020 through 8 May 2020

ER -

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

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