Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings

Raffaele Pojer, Andrea Passerini, Manfred Jaeger

Keywords: Graph neural networks, statistical relational learning, relational Bayesian networks, neuro-symbolic integration, explanation

Abstract:

Graph neural networks (GNNs) and statistical relational learning are two different approaches to learning with
graph data. The former can provide highly accurate models for specific tasks when sufficient training
data is available, whereas the latter supports a wider range of reasoning types, and can incorporate
manual specifications of interpretable domain knowledge. In this paper we present a method to embed
GNNs in a statistical relational learning framework, such that the predictive model
represented by the GNN becomes part of a full generative model. This model then supports a wide range of queries,
including general conditional probability queries, and computing most probable configurations of
unobserved node attributes or edges.
In particular, we demonstrate how this latter type of queries can be used to obtain model-level
explanations of a GNN in a flexible and interactive manner.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Submission Number: 122