Causal Spherical Hypergraph Networks for Modelling Social Uncertainty

2025-06-21

AI Summary

This study addresses key challenges in modeling human behavior within dynamic social environments: uncertainty quantification, causal confounding, and coupled group interactions. To this end, we propose the first unified predictive framework integrating higher-order structural modeling, directional causality, and cognitive uncertainty. Our approach jointly leverages hyperspherical embeddings—using the von Mises–Fisher distribution to capture semantic geometry and belief uncertainty—hypergraph structures to model multi-agent group interactions, and Granger causality to identify temporal influence pathways. We further introduce an angular message-passing mechanism and a Granger-guided subgraph construction strategy to enable robust, causally aware information propagation. Extensive experiments on SNARE, PHEME, and AMIGOS demonstrate significant improvements over state-of-the-art baselines in prediction accuracy, noise robustness, and probabilistic calibration. Moreover, our framework supports interpretable social behavior attribution through explicit causal and uncertainty-aware reasoning.

Technology Category

• Reasoning under Uncertainty: Causality
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Human social behaviour is governed by complex interactions shaped by uncertainty, causality, and group dynamics. We propose Causal Spherical Hypergraph Networks (Causal-SphHN), a principled framework for socially grounded prediction that jointly models higher-order structure, directional influence, and epistemic uncertainty. Our method represents individuals as hyperspherical embeddings and group contexts as hyperedges, capturing semantic and relational geometry. Uncertainty is quantified via Shannon entropy over von Mises-Fisher distributions, while temporal causal dependencies are identified using Granger-informed subgraphs. Information is propagated through an angular message-passing mechanism that respects belief dispersion and directional semantics. Experiments on SNARE (offline networks), PHEME (online discourse), and AMIGOS (multimodal affect) show that Causal-SphHN improves predictive accuracy, robustness, and calibration over strong baselines. Moreover, it enables interpretable analysis of influence patterns and social ambiguity. This work contributes a unified causal-geometric approach for learning under uncertainty in dynamic social environments.

Problem

• Modeling social uncertainty with higher-order interactions
• Quantifying epistemic uncertainty in group dynamics
• Identifying temporal causal dependencies in social behavior

Innovation

• Hyperspherical embeddings for individual representation
• Granger-informed subgraphs for causal dependencies
• Angular message-passing for information propagation