Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

TL;DR

This paper introduces an interpretable graph convolutional neural network with a regularized attention mechanism that enhances link prediction performance on noisy biomedical knowledge graphs, offering visualization tools for model interpretation and dataset denoising.

Contribution

It presents a novel regularized attention mechanism for GCNNs that improves robustness to noise and provides visualization methods for interpretability and dataset denoising in biomedical KGs.

Findings

Improved link prediction on noisy datasets

Effective visualization of learned representations

Demonstrated potential for biomedical target discovery

Abstract

In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favorably accommodates noise in KGs, a pervasive issue in real-world applications. Further, we explore new visualization methods for interpretable modelling and to illustrate how the learned representation can be exploited to automate dataset denoising. The results are demonstrated on a synthetic dataset, the common benchmark dataset FB15k-237, and a large biomedical knowledge graph derived from a combination of noisy and clean data sources. Using these improvements, we visualize a learned model's representation of the disease cystic fibrosis and demonstrate how to interrogate a neural network to show the potential of PPARG as a candidate therapeutic target for rheumatoid arthritis.