Interpretable Graph Neural Networks for Tabular Data

arXiv:2308.08945v2 Announce Type: replace
Abstract: Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to …

abstract applications arxiv box cs.ai cs.lg data feature form format gnns graph graph neural networks however interactions networks neural networks representation representation learning tabular tabular data through type world