Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning
This work addresses a specific bottleneck in graph neural networks for researchers in graph-based machine learning, presenting an incremental improvement.
The paper tackles the problem of noise in node features affecting graph structure generation in Latent Graph Inference for Graph Neural Networks, introducing Probability Passing to refine the graph structure and showing effectiveness in experiments.
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.