Graph Neural Networks (GNNs) have achieved notable success in the analysis of non-Euclidean data across a wide range of domains. However, their applicability is constrained by the dependence on the observed graph structure. To solve this problem, Latent Graph Inference (LGI) is proposed to infer a task-specific latent structure by computing similarity or edge probability of node features and then apply a GNN to produce predictions. Even so, existing approaches neglect the noise from node features, which affects generated graph structure and performance. In this work, we introduce a novel method called Probability Passing to refine the generated graph structure by aggregating edge probabilities of neighboring nodes based on observed graph. Furthermore, we continue to utilize the LGI framework, inputting the refined graph structure and node features into GNNs to obtain predictions. We name the proposed scheme as Probability Passing-based Graph Neural Network (PPGNN). Moreover, the anchor-based technique is employed to reduce complexity and improve efficiency. Experimental results demonstrate the effectiveness of the proposed method.

翻译：图神经网络（GNNs）在众多领域的非欧几里得数据分析中取得了显著成功。然而，其应用受限于对观测图结构的依赖。为解决此问题，潜在图推断（LGI）被提出，通过计算节点特征的相似性或边概率来推断任务特定的潜在结构，随后应用GNN进行预测。尽管如此，现有方法忽略了节点特征中的噪声，这会影响生成的图结构及性能。本文提出一种名为概率传递的新方法，基于观测图通过聚合相邻节点的边概率来优化生成的图结构。进一步地，我们继续利用LGI框架，将优化后的图结构与节点特征输入GNN以获得预测。我们将所提方案命名为基于概率传递的图神经网络（PPGNN）。此外，采用基于锚点的技术以降低复杂度并提升效率。实验结果验证了所提方法的有效性。