A Self-Explainable Heterogeneous GNN for Relational Deep Learning

Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger

Abstract:

Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existing GNN methods struggle with the complexity of the heterogeneous graphs induced by databases with numerous tables and relations.
Traditional approaches either consider all possible relational meta-paths, thus failing to scale with the number of relations, or rely on domain experts to identify relevant meta-paths. A recent solution does manage to learn informative meta-paths without expert supervision,
but assumes that a node’s class depends solely on the existence of a meta-path occurrence.
In this work, we present a self-explainable heterogeneous GNN for relational data,
that supports models in which class membership depends on aggregate information obtained from multiple
occurrences of a meta-path.
Experimental results show that in the context of relational databases, our approach effectively identifies informative meta-paths that faithfully capture the model’s reasoning mechanisms. It significantly outperforms existing methods in both synthetic and real-world scenarios.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/francescoferrini/MPS-GNN

Assigned Action Editor: ~Simone_Scardapane1

Submission Number: 3840