弥合合理性与可接受性之间的差距：动态图系统的约束感知流图
Learning and Structurally Validating Simulation Scenario Continuations in Dynamic Graph Systems
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中文总结 AI 辅助
研究探讨采样后符号约束对动态图系统生成轨迹建模可靠性的影响，用条件扩散模型结合外部符号层，在两个合成场景评估，表明统计合理性与结构可接受性不同，图结构复杂时符号约束处理更重要。
中文摘要 AI 辅助
生成模型可通过生成合理的未来系统轨迹集合来支持不确定性下的决策，但统计合理性并不能确保结构可行性。本研究探讨采样后符号约束能否提高动态图结构系统中生成轨迹建模的可靠性。条件扩散模型从部分观测生成未来图状态轨迹，外部符号层应用硬过滤、软加权或基于投影的修复。该框架在两个受控合成场景下评估，使用结构有效性、样本效率等指标。结果表明统计合理性和结构可接受性是不同的可靠性属性，且随着图结构复杂性增加，符号约束处理更有价值。
英文摘要
Data-driven generative models can extend partially observed simulation trajectories into ensembles of alternative future scenarios. However, consistency with a learned trajectory distribution does not ensure that generated continuations satisfy the structural conditions of the simulated system. This paper presents a method for learned scenario continuation and post-generation structural validation in dynamic graph simulations. A conditional diffusion model generates future graph-state trajectories from partial histories, while an external symbolic layer evaluates each continuation using a Boolean admissibility indicator and a continuous violation score. This information supports hard filtering and soft weighting, with optional projection considered as a deterministic repair baseline. The method is evaluated on two controlled dynamic-graph regimes sharing the same continuation architecture and training protocol but differing in dimensionality and dependency complexity. Evaluation considers invalid probability mass, scenario retention, effective sample size, diversity, robustness, and calibration. In the compact positive-control regime, unconstrained invalid mass is 0.002996, indicating near-complete overlap between the learned and admissible scenario spaces. In the medium-complexity regime, invalid mass rises to 0.155929. Hard filtering removes all invalid scenarios while retaining 84.4% of generated continuations. Soft weighting preserves an effective sample size ratio of 0.998764 but reduces invalid mass only to 0.148807. These results show that learned-distribution support, structural admissibility, and probability calibration can diverge and should therefore be assessed separately in learned simulation-scenario generation and management.
