Consistent tie-strength labeling for multilayer strong triadic closure

Abstract Inferring tie strengths ( strong vs. weak ) is a core task in network analysis, often guided by the Strong Triadic Closure (STC) principle. In multilayer networks, such as social platforms or biological systems, applying STC independently to each layer can lead to inconsistent tie labels, undermining interpretations that rely on coherent relationship semantics across layers. We propose new formulations, multilayer STC and its extension STC+, which are axiomatically grounded and enforce cross-layer consistency. These problems are NP-hard; we present efficient 2- and 6-approximation algorithms alongside exact solutions. Experiments on real-world networks demonstrate that our methods produce consistent tie strength labelings with a transparent structural justification, significantly improving over the baselines.

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Publication Details

Journal
Data Mining and Knowledge Discovery
Published
2026-05-09
DOI
https://doi.org/10.1007/s10618-026-01216-9
Primary Topic
Advanced Graph Neural Networks
Type
article
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article

Consistent tie-strength labeling for multilayer strong triadic closure

Athanasios L. Konstantinidis, Lutz Oettershagen, Fariba Ranjbar, Giuseppe F. Italiano
Data Mining and Knowledge Discovery
Advanced Graph Neural Networks
article

Consistent tie-strength labeling for multilayer strong triadic closure

Athanasios L. Konstantinidis, Lutz Oettershagen, Fariba Ranjbar, Giuseppe F. Italiano
article en

Abstract

Abstract Inferring tie strengths ( strong vs. weak ) is a core task in network analysis, often guided by the Strong Triadic Closure (STC) principle. In multilayer networks, such as social platforms or biological systems, applying STC independently to each layer can lead to inconsistent tie labels, undermining interpretations that rely on coherent relationship semantics across layers. We propose new formulations, multilayer STC and its extension STC+, which are axiomatically grounded and enforce cross-layer consistency. These problems are NP-hard; we present efficient 2- and 6-approximation algorithms alongside exact solutions. Experiments on real-world networks demonstrate that our methods produce consistent tie strength labelings with a transparent structural justification, significantly improving over the baselines.

Data Mining and Knowledge DiscoveryVol. 40(4)
University of Liverpool (GB), University of Ioannina (GR), Libera Università Internazionale degli Studi Sociali Guido Carli (IT)
Reduced inequalities
Openalex Percentile: Top 4%
Advanced Graph Neural Networks
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