用贝叶斯方法建模图结构不确定性，提升异质图上的半监督学习性能。
Sparse Bayesian Message Passing under Structural Uncertainty
- 通过后验分布建模有符号邻接矩阵，允许边为正、负或缺失
- 在合成与真实噪声下，优于主流基线模型，在异质图上准确率提升显著
- 适合处理标签异质性高、边噪声大的实际图数据场景
现实世界图数据的半监督学习常面临异质性问题，即观测图不可靠或标签异质。现有图神经网络多依赖固定邻接结构，或通过正则化处理结构噪声。本文显式建模有符号邻接矩阵的后验分布，使每条边可为正、负或不存在。提出稀疏有符号消息传递网络，从贝叶斯视角自然抵御边噪声与异质性。结合（i）有符号图结构的后验边际化与（ii）稀疏有符号消息聚合，提供一种严谨处理边噪声与异质性的方法。实验表明，该方法在含合成与真实结构噪声的异质性基准上均优于强基线模型。
原文摘要 · Abstract (English)
Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural networks either rely on a fixed adjacency structure or attempt to handle structural noise through regularization. In this work, we explicitly capture structural uncertainty by modeling a posterior distribution over signed adjacency matrices, allowing each edge to be positive, negative, or absent. We propose a sparse signed message passing network that is naturally robust to edge noise and heterophily, which can be interpreted from a Bayesian perspective. By combining (i) posterior marginalization over signed graph structures with (ii) sparse signed message aggregation, our approach offers a principled way to handle both edge noise and heterophily. Experimental results demonstrate that our method outperforms strong baseline models on heterophilic benchmarks under both synthetic and real-world structural noise.
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