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

Conference paper (2020)

Authors

Mario Coutino Signal Processing Systems - EEMCS
Georgios V Karanikolas University of Minnesota
G.J.T. Leus Signal Processing Systems - EEMCS
Georgios B. Giannakis University of Minnesota
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Mario Coutino, Georgios V Karanikolas, G.J.T. Leus, Georgios B. Giannakis
DOI
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https://doi.org/10.1109/ICASSP40776.2020.9053655
Link prediction Structural equation models Volterra series Higher-order interactions Network data models
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.