用球面超图建模社交不确定性，提升预测准确性与可解释性
Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
- 将个体和群体关系建模为球面嵌入与超边，融合方向性与不确定性
- 在三个社交数据集上，预测准确率与校准度优于主流基线方法
- 适合研究社交影响、群体行为或不确定环境下的机器学习应用
人类社交行为受不确定性、因果关系与群体动态的复杂交互影响。我们提出因果球面超图网络（Causal-SphHN），一种基于社会情境的预测框架，联合建模高阶结构、方向性影响与认知不确定性。个体以球面嵌入表示，群体上下文作为超边，捕捉语义与关系几何。通过冯·米塞斯-费舍尔分布的香农熵量化不确定性，利用格兰杰启发的子图识别时序因果依赖。信息通过角度消息传递机制传播，保留信念分散性与方向语义。在SNARE（离线网络）、PHEME（在线话语）和AMIGOS（多模态情感）上的实验表明，Causal-SphHN在预测准确率、鲁棒性和校准度方面均优于强基线。同时支持影响模式与社交模糊性的可解释分析。本工作提出了动态社交环境中学习不确定性的统一因果-几何方法。
原文摘要 · Abstract (English)
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.
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