Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning

TL;DR

This paper introduces Probability Passing, a method to refine graph structures in GNNs by aggregating edge probabilities, enhancing performance in non-Euclidean data analysis.

Contribution

It proposes Probability Passing to improve latent graph inference by reducing noise and integrating edge probability aggregation within GNNs.

Findings

Improved accuracy over baseline GNNs.

Effective noise reduction in graph structure inference.

Enhanced efficiency with anchor-based technique.

Abstract

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.