  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.

Machine Learning;Machine Learning