TREE-G: Decision Trees Contesting Graph Neural Networks

arXiv:2207.02760v5 Announce Type: replace
Abstract: When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability properties. However, when it comes to graph-structured data, it is not clear how to apply them effectively, in a way that incorporates the topological information with the tabular data available on the vertices of the graph. To address this challenge, we introduce TREE-G. TREE-G modifies standard …

abstract accuracy application apply arxiv clear cs.ai cs.lg data decision decision trees explainability graph graph neural networks networks neural networks popular structured data tabular tabular data them tree trees type types