Non-Parametric Graph Learning for Bayesian Graph Neural Networks

TL;DR

This paper introduces a flexible non-parametric Bayesian framework for learning the posterior distribution of graph structures, improving graph representation in neural networks for tasks like classification and prediction.

Contribution

It proposes a novel non-parametric graph model that effectively captures true relationships and scales well, enhancing graph neural network performance.

Findings

Improved node classification accuracy

Enhanced link prediction performance

Effective in recommendation systems

Abstract

Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. However, the graph used by these algorithms is often constructed based on inaccurate modelling assumptions and/or noisy data. As a result, it fails to represent the true relationships between nodes. A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial. In this paper, we propose a novel non-parametric graph model for constructing the posterior distribution of graph adjacency matrices. The proposed model is flexible in the sense that it can effectively take into account the output of graph-based learning algorithms that target specific tasks. In addition, model inference scales well to large graphs. We demonstrate the advantages of this model in three different problem settings: node classification, link prediction and recommendation.