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.

翻译：现实世界图上的半监督学习经常面临异质性的挑战，即观测图不可靠或标签非匹配。许多现有的图神经网络要么依赖固定的邻接结构，要么试图通过正则化处理结构噪声。在本工作中，我们通过建模有符号邻接矩阵的后验分布来显式捕捉结构不确定性，允许每条边为正、负或缺失。我们提出了一种稀疏有符号消息传递网络，该网络天然对边噪声和异质性具有鲁棒性，并可从贝叶斯视角进行解释。通过将（i）有符号图结构的后验边缘化与（ii）稀疏有符号消息聚合相结合，我们的方法为处理边噪声和异质性提供了原则性方案。实验结果表明，在合成和真实世界结构噪声下的异质性基准测试中，我们的方法优于强基线模型。