Multiplex Hypergraph Modeling of Higher Order Structures in Psychometric Networks

2026-04-24

AI Summary

Traditional psychometric networks capture only pairwise symptom associations, limiting their ability to model higher-order dependencies that reflect synergistic or redundant mechanisms. This work proposes an information-theoretic multiplex hypergraph framework that, for the first time, integrates Ω-information with multiplex hypergraphs to quantify synergistic and redundant structures among symptom clusters in eating disorders. By combining network topology with theoretically grounded subscale-based selection of candidate symptom groups, and employing a three-stage inference pipeline—including null-model testing and bootstrap-based robustness evaluation—the study constructs a hierarchical multiplex hypergraph. The results reveal a transdiagnostically stable core of higher-order synergy alongside diagnosis-specific combinatorial patterns, with redundancy predominantly localized within eating behavior and body image dimensions.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Computational social science

Abstract

Psychiatric disorders have been traditionally conceptualized as latent conditions producing observable symptoms, but recent studies suggest that psychopathology may emerge from symptoms interactions. Psychometric networking model these relations focusing on pairwise associations but overlooks higher-order dependencies arising among groups of variables. These dependencies may reflect synergistic mechanisms, where joint symptom configurations convey more information than pairwise relations, or redundancy, where information overlaps. We introduce an information-theoretic multiplex hypergraph framework to identify and compare higher-order interactions in eating disorders data, across diagnostic groups (e.g., anorexia nervosa). Higher-order structures are quantified using $Ω$-information, a measure that captures the balance between redundancy and synergy. To address the combinatorial growth of candidate subsets, multiple testing and estimation instability, we propose a structured pipeline comprising: (i) targeted candidate selection based on dyadic network topology and theory-driven subscale information; (ii) a three-stage inferential procedure combining null-model testing with bootstrap robustness assessment; and (iii) the construction and analysis of diagnosis-layered, synergistic and redundant multiplex hypergraphs. Results highlight how synergy captures the emergent, higher-order organization of diagnoses, revealing both a stable transdiagnostic core and diagnosis-specific ways in which these domains combine. By contrast, redundancy is confined to eating and body-image related content, marking reinforcement rather than broader symptom integration.

Problem

• higher-order interactions
• psychometric networks
• synergy
• redundancy
• eating disorders

Innovation

• multiplex hypergraph
• higher-order interactions
• Ω-information
• synergy and redundancy
• psychometric networks