修正图模型误设问题，提升贝叶斯推断的可靠性
Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
- 用广义后验和条件独立性改进图生成模型推断
- 在真实与合成网络上实现更准的链接预测与校准
- 适合处理几何不符、结构异常的复杂网络
贝叶斯潜在空间模型为网络表征提供了合理框架，但依赖于几何结构和连接函数的正确设定。现实网络常违背这些假设，导致几何不匹配与结构异常，破坏标准度量性质。我们证明此类误设会使数据生成分布脱离模型类别，使贝叶斯推断过度自信且校准不良。为此，我们提出针对随机几何图的广义后验框架，引入链式顺序R-SafeBayes方法，利用二元条件独立性估计预序风险，并自适应调节后验正则化。在合成与真实网络上的实验表明，该方法显著提升校准度、链接预测性能，并提供可靠准则以选择欧氏、球面或双曲空间中的潜在几何。
原文摘要 · Abstract (English)
Bayesian latent space models offer a principled approach to network representation, but rely on correct specification of both geometry and link function. Real-world networks often violate these assumptions, exhibiting geometric mismatch and structural anomalies that break standard metric properties. We show that such misspecification pushes the data-generating distribution outside the model class, causing Bayesian inference to become overconfident and poorly calibrated. To address this, we propose a generalized posterior framework for random geometric graphs. We introduce Link-Sequential R-SafeBayes, a method that exploits dyadic conditional independence to estimate prequential risk and adaptively tune posterior regularization. Experiments on synthetic and real-world networks demonstrate improved calibration, better link prediction performance, and a reliable criterion for selecting latent geometries across Euclidean, spherical, and hyperbolic spaces.
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