基于算子的合成系统性风险动态可视化分析流水线
An Operator-Based Visual Analytics Pipeline for Synthetic Systemic Risk Dynamics
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中文总结 AI 辅助
本文提出一个基于六种算子的模块化可视化分析流水线，将合成金融数据转化为动态可视化，以透明方式连接风险建模、网络动力学与可视化，支持可复现地探索系统性风险。
中文摘要 AI 辅助
本文提出了一种基于算子的可视化分析流水线，用于探索金融网络中的合成系统性风险动态。该框架被表述为一系列数学算子的组合，这些算子依次将合成的金融观测数据转换为动态的科学可视化。该流水线由六个算子组成：潜在风险映射、概率评分生成、金融网络构建、基于距离的传染动态、视觉编码和透视投影。这些算子共同提供了一个模块化的计算结构，将非线性风险曲面、时间相关的概率状态、网络拓扑和冲击传播联系起来。一个可复现的实现通过受控的合成实验展示了该架构。非线性的潜在风险表示被转换为概率评分，嵌入到加权金融网络中，并通过最短路径传染进行传播。随后，所得状态被映射为动态视觉表示。实验表明，可视化可以被视为分析过程中的一个明确阶段，而非后处理步骤。所提出的公式并不旨在引入新的预测模型或传染机制。相反，它提供了一个透明且模块化的流水线，在统一的基于算子的架构中连接了生成式风险建模、网络动力学和科学可视化。该方法支持可复现性，并促进了在合成环境中对复杂系统性风险过程的探索和交流。
英文摘要
This work presents an operator-based visual analytics pipeline for exploring synthetic systemic risk dynamics in financial networks. The framework is formulated as a composition of mathematical operators that sequentially transform synthetic financial observations into dynamic scientific visualizations. The pipeline consists of six operators: latent risk mapping, probabilistic score generation, financial network construction, distance-based contagion dynamics, visual encoding, and perspective projection. Together, these operators provide a modular computational structure linking nonlinear risk surfaces, time-dependent probabilistic states, network topology, and shock propagation. A reproducible implementation demonstrates the architecture through controlled synthetic experiments. Nonlinear latent risk representations are converted into probabilistic scores, embedded in a weighted financial network, and propagated via shortest-path contagion. The resulting states are then mapped into dynamic visual representations. The experiments illustrate how visualization can be treated as an explicit stage of the analytical process rather than a post-processing step. The proposed formulation does not aim to introduce new predictive models or contagion mechanisms. Instead, it offers a transparent and modular pipeline that connects generative risk modeling, network dynamics, and scientific visualization within a unified operator-based architecture. This approach supports reproducibility and facilitates the exploration and communication of complex systemic-risk processes in synthetic settings.
发表机构
- Institute of Mathematics and Statistics, University of São Paulo（圣保罗大学数学与统计学院）
机构由 AI 辅助整理，请以论文原文为准。
