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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3887-3891
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

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

Keywords

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

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