Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs. Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes. Although these methods have achieved superior performance, how to exactly exploit the complex data correlations among nodes to reconstruct missing node features is still a great challenge. To solve the above problem, we propose a self-supervised guided hypergraph feature propagation (SGHFP). Specifically, the feature hypergraph is first generated according to the node features with missing information. And then, the reconstructed node features produced by the previous iteration are fed to a two-layer GNNs to construct a pseudo-label hypergraph. Before each iteration, the constructed feature hypergraph and pseudo-label hypergraph are fused effectively, which can better preserve the higher-order data correlations among nodes. After then, we apply the fused hypergraph to the feature propagation for reconstructing missing features. Finally, the reconstructed node features by multi-iteration optimization are applied to the downstream semi-supervised classification task. Extensive experiments demonstrate that the proposed SGHFP outperforms the existing semi-supervised classification with missing node feature methods.

翻译：图神经网络（GNNs）在处理缺失节点特征时近期受到广泛关注，此类缺失特征严重损害了现有GNNs的性能。现有方法通过已知与未知属性节点间的信息传播来重构缺失节点特征，虽取得了优异效果，但如何精准利用节点间复杂的数据相关性进行特征重构仍是重大挑战。为此，我们提出自监督引导超图特征传播（SGHFP）。具体而言：首先根据含有缺失信息的节点特征生成特征超图，接着将前次迭代产生的重构节点特征输入两层GNNs构建伪标签超图。每次迭代前，将所构建的特征超图与伪标签超图进行有效融合，以更好保留节点间的高阶数据关联。随后利用融合超图进行特征传播以重构缺失特征。通过多轮迭代优化获得的重构节点特征最终应用于下游半监督分类任务。大量实验表明，所提出的SGHFP方法在缺失节点特征的半监督分类任务中优于现有方法。