因果球面超图网络用于建模社会不确定性
Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
AI总结：
提出因果球面超图网络（Causal-SphHN），通过超球面嵌入、超边、香农熵和格兰杰因果子图，联合建模高阶结构、方向影响和认知不确定性，在多个数据集上提升预测性能并支持可解释分析。
摘要：
人类的社会行为由不确定性、因果关系和群体动态所塑造的复杂交互所支配。我们提出了因果球面超图网络（Causal-SphHN），这是一个用于社会基础预测的原则性框架，它联合建模了高阶结构、方向性影响和认知不确定性。我们的方法将个体表示为超球面嵌入，将群体上下文表示为超边，从而捕获语义和关系几何。不确定性通过von Mises-Fisher分布上的香农熵进行量化，而时间因果依赖则通过基于格兰杰因果的子图来识别。信息通过一种尊重信念离散度和方向语义的角度消息传递机制进行传播。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在预测准确性、鲁棒性和校准方面优于强基线。此外，它还能对影响模式和社会模糊性进行可解释的分析。这项工作为在动态社会环境中不确定性下的学习提供了一种统一的因果几何方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
