Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data, yet their effectiveness often depends on the availability of complete node features. In many real-world scenarios, however, feature matrices are highly sparse or contain sensitive information, leading to degraded performance and increased privacy risks. Furthermore, direct exposure of information can result in unintended data leakage, enabling adversaries to infer sensitive information. To address these challenges, we propose a novel Multi-view Feature Propagation (MFP) framework that enhances node classification under feature sparsity while promoting privacy preservation. MFP extends traditional Feature Propagation (FP) by dividing the available features into multiple Gaussian-noised views, each propagating information independently through the graph topology. The aggregated representations yield expressive and robust node embeddings. This framework is novel in two respects: it introduces a mechanism that improves robustness under extreme sparsity, and it provides a principled way to balance utility with privacy. Extensive experiments conducted on graph datasets demonstrate that MFP outperforms state-of-the-art baselines in node classification while substantially reducing privacy leakage. Moreover, our analysis demonstrates that propagated outputs serve as alternative imputations rather than reconstructions of the original features, preserving utility without compromising privacy. A comprehensive sensitivity analysis further confirms the stability and practical applicability of MFP across diverse scenarios. Overall, MFP provides an effective and privacy-aware framework for graph learning in domains characterized by missing or sensitive features.

翻译：图神经网络（GNNs）在关系数据的节点分类任务中已展现出卓越性能，但其效果往往依赖于完整节点特征的可用性。然而，在许多实际场景中，特征矩阵高度稀疏或包含敏感信息，导致性能下降与隐私风险增加。此外，信息的直接暴露可能引发非预期的数据泄露，使攻击者能够推断敏感信息。为应对这些挑战，我们提出了一种新颖的多视图特征传播（MFP）框架，该框架在提升特征稀疏条件下节点分类性能的同时促进隐私保护。MFP通过将可用特征划分为多个添加高斯噪声的视图，扩展了传统特征传播（FP）方法，每个视图通过图拓扑独立传播信息。聚合后的表示可产生表达力强且鲁棒的节点嵌入。该框架的创新性体现在两方面：其一，引入了在极端稀疏条件下提升鲁棒性的机制；其二，提供了平衡效用与隐私的原则性方法。在图数据集上的大量实验表明，MFP在节点分类任务中优于现有先进基线方法，同时显著降低了隐私泄露风险。此外，我们的分析证明传播输出可作为原始特征的替代插补而非重建，在保护隐私的同时保持了实用性。全面的敏感性分析进一步证实了MFP在不同场景下的稳定性与实际适用性。总体而言，MFP为特征缺失或敏感特征领域的图学习提供了一个高效且具备隐私意识的框架。