HetSAGE: Heterogenous Graph Neural Network for Relational Learning
Vince Jankovics, Michael Garcia-Ortiz, Eduardo Alonso · City Research Online (City University London) · 2021
This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way how information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that on CORA, MUTA188 and MovieLens our approach outperforms the state-of-the-art in NSL.