Interpretable Graph Neural Networks for Heterogeneous Tabular Data

2024-08-14 · Amr AlKhatib, Henrik Boström

Many machine learning algorithms for tabular data produce black-box models, which prevent users from understanding the rationale behind the model predictions. In their unconstrained form, graph neural networks fall into this category, and they have further limited abilities to handle heterogeneous data. To overcome these limitations, an approach is proposed, called IGNH (Interpretable Graph Neural Network for Heterogeneous tabular data), which handles both categorical and numerical features, while constraining the learning process to generate exact feature attributions together with the predictions. A large-scale empirical investigation is presented, showing that the feature attributions provided by IGNH align with Shapley values that are computed post hoc. Furthermore, the results show that IGNH outperforms two powerful machine learning algorithms for tabular data, Random Forests and TabNet, while reaching a similar level of performance as XGBoost.

📄 PDF Abstract BibTeX arXiv:2408.07661

Code (2)

amrmalkhatib/ignh 공식 구현 pytorch

amrmalkhatib/ignnet pytorch

Tasks

Graph Neural Network

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