Causal Spherical Hypergraph Networks for Modelling Social Uncertainty

• URL: http://arxiv.org/abs/2506.17840v1
• Date: Sat, 21 Jun 2025 22:30:04 GMT
• Title: Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
• Abstract summary: We propose Causal Spherical Hypergraph Networks (Causal-SphHN), a principled framework for socially grounded prediction.<n>Our method represents individuals as hyperspherical embeddings and group contexts as hyperedges, capturing semantic and relational geometry.<n>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.
• Score: 3.0181801777983086
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Teacher Knows It Best: Spontaneous Symmetry Breaking and Tipping Points in Networked Langevin Dynamics AI Sycophancy [5.868852957948178]
  We formulate a statistical physics framework to model a networked dynamical system exhibiting bistability, driven by additive noise and social conformity.<n>We apply this model to understand and mitigate AI-induced delusional spiraling-a phenomenon where algorithmic sycophancy from Large Language Models continuously reinforces inaccurate beliefs.<n>We prove mathematically that under certain conditions, a highly concentrated, rapid intervention targeting massive hubs strictly outperforms a distributed, slow approach to rescue the network.
  arXiv  Detail & Related papers  (2026-07-27T11:47:57Z)
• From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond [7.419398719484998]
  Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects.<n>This paper introduces a novel framework inspired by statistical mechanics that instead models variable dependencies through an undirected, energy-based representation.<n>We empirically examined our framework through simulations on an industrial IoT testbed with hybrid continuous and discrete variables.
  arXiv  Detail & Related papers  (2026-07-06T18:59:27Z)
• SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry [7.816699755198432]
  We introduce SphUnc, a unified framework combining hyperspherical representation learning with structural causal modeling.<n>A structural causal model on spherical latents enables directed influence identification and interventional reasoning via sample-based simulation.<n> Empirical evaluations on social and affective benchmarks demonstrate improved accuracy, better calibration, and interpretable causal signals.
  arXiv  Detail & Related papers  (2026-03-01T16:11:49Z)
• On the Plasticity and Stability for Post-Training Large Language Models [54.757672540381236]
  We identify a root cause as the conflict between plasticity and stability gradients.<n>We propose Probabilistic Conflict Resolution (PCR), a framework that models gradients as random variables.<n>PCR significantly smooths the training trajectory and achieves superior performance in various reasoning tasks.
  arXiv  Detail & Related papers  (2026-02-06T07:31:26Z)
• Human-like Social Compliance in Large Language Models: Unifying Sycophancy and Conformity through Signal Competition Dynamics [7.209622481153123]
  This study introduces the Signal Competition Mechanism, a unified framework validated by assessing behavioral correlations across 15 Large Language Models.<n>The transition to compliance is shown to be a deterministic process governed by a linear boundary, where the Social Emotional Signal effectively suppresses the Information Signal.
  arXiv  Detail & Related papers  (2025-12-25T06:57:42Z)
• CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality [4.936998293690288]
  CausalMamba is a novel framework that integrates Mamba-based sequence modeling, graph convolutional networks (GCNs), and differentiable causal discovery via NOTEARS.<n>Our framework provides a unified approach for rumor classification and influence analysis, paving the way for more explainable and actionable misinformation detection systems.
  arXiv  Detail & Related papers  (2025-11-20T09:59:16Z)
• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series [0.4369550829556578]
  We introduce Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in MTS (DyCAST-Net)<n>DyCAST-Net is a novel architecture designed to enhance causal discovery by integrating dilated temporal convolutions and dynamic sparse attention mechanisms.<n>We show that DyCAST-Net consistently outperforms existing models such as TCDF, GCFormer, and CausalFormer.
  arXiv  Detail & Related papers  (2025-07-13T01:03:27Z)
• Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity [22.491097360752903]
  We advocate for a causal learning perspective to advance anomaly detection in spatially distributed infrastructures.<n>We identify and formalize three key directions: causal graph profiling, multi-view fusion, and continual causal graph learning.<n>Our objective is to lay a new research trajectory toward scalable, adaptive, explainable, and spatially grounded anomaly detection systems.
  arXiv  Detail & Related papers  (2025-07-10T21:19:28Z)
• The Relativity of Causal Knowledge [4.051523221722475]
  Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning.<n>We introduce the relativity of causal knowledge, which posits structural causal models (SCMs) are inherently imperfect, subjective representations embedded within networks of relationships.
  arXiv  Detail & Related papers  (2025-03-13T16:24:48Z)
• Topology-Aware Conformal Prediction for Stream Networks [54.505880918607296]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• An AI-powered Bayesian generative modeling approach for causal inference in observational studies [4.624176903641013]
  CausalBGM is an AI-powered Bayesian generative modeling approach.<n>It estimates the individual treatment effect (ITE) by learning individual-specific distributions of a low-dimensional latent feature set.
  arXiv  Detail & Related papers  (2025-01-01T06:52:45Z)
• Unified Causality Analysis Based on the Degrees of Freedom [1.2289361708127877]
  This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application.
  arXiv  Detail & Related papers  (2024-10-25T10:57:35Z)
• Identifiable Latent Neural Causal Models [82.14087963690561]
  Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. We determine the types of distribution shifts that do contribute to the identifiability of causal representations. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations.
  arXiv  Detail & Related papers  (2024-03-23T04:13:55Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Adversarial Socialbots Modeling Based on Structural Information Principles [24.339397435628214]
  Socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. We propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors.
  arXiv  Detail & Related papers  (2023-12-13T12:32:12Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Adversarial Robustness through the Lens of Causality [105.51753064807014]
  adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. We propose to incorporate causality into mitigating adversarial vulnerability. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability.
  arXiv  Detail & Related papers  (2021-06-11T06:55:02Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.