Joint Link Prediction Via Inference from a Model

Parmis Naddaf, Erfaneh Mahmoudzaheh Ahmadi Nejad, Kiarash Zahirnia, Manfred Jaeger, Oliver Schulte

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstract

A Joint Link Prediction Query (JLPQ) specifies a set of links to be predicted, given another set of links as well as node attributes as evidence. While single link prediction has been well studied in literature on deep graph learning, predicting multiple links together has gained little attention. This paper presents a novel framework for computing JLPQs using a probabilistic deep Graph Generative Model. Specifically, we develop inference procedures for an inductively trained Variational Graph Auto-Encoder (VGAE) that estimates the joint link probability for any input JLPQ, without
retraining. For evaluation, we apply inference to a range of joint link prediction queries on six benchmark datasets. We find that for most datasets and query types, joint link prediction via inference from a model achieves good predictive performance, better than the independent link prediction baselines (by 0.02-0.4 AUC points
depending on the dataset).
OriginalsprogEngelsk
TitelCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Antal sider10
ForlagAssociation for Computing Machinery
Publikationsdato21 okt. 2023
Sider1877-1886
ISBN (Elektronisk)979-8-4007-0124-5
DOI
StatusUdgivet - 21 okt. 2023
Begivenhed32nd ACM International Conference on Information and Knowledge Management - Birmingham, Storbritannien
Varighed: 21 okt. 202325 okt. 2023

Konference

Konference32nd ACM International Conference on Information and Knowledge Management
Land/OmrådeStorbritannien
ByBirmingham
Periode21/10/202325/10/2023

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