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/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

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
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
PublisherIEEE
Pages3887-3891
Number of pages5
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
DOIs
Publication statusPublished - 2020
EventICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020

Conference

ConferenceICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

Keywords

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

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