Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

2018/12/01 by Daniel Neil, Neil, Daniel, Joss Briody +9 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics

paper · pdf · doi:10.48550/arxiv.1812.00279

openalex publication_date 2018/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Semi-Supervised Classification with Graph Convolutional Networks
• DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
• Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
• Modeling Relational Data with Graph Convolutional Networks

Cited by

• Catching Contamination Before Generation: Spectral Kill Switches for Agents
• Genotype-Phenotype Integration through Machine Learning and Personalized Gene Regulatory Networks for Cancer Metastasis Prediction
• GNNExplainer: Generating Explanations for Graph Neural Networks

Related

• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
• Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learning
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
• Information Flow in Graph Neural Networks: A Clinical Triage Use Case
• Knowledge Representation in Graphs using Convolutional Neural Networks