Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
This work addresses the challenge of learning under uncertainty in dynamic social environments for applications such as social network analysis and affect prediction, representing an incremental advancement by integrating existing concepts like hypergraphs and causality into a unified framework.
The paper tackled the problem of modeling complex social interactions with uncertainty and causality by proposing Causal Spherical Hypergraph Networks, which improved predictive accuracy, robustness, and calibration over strong baselines in experiments on datasets like SNARE, PHEME, and AMIGOS.
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.