论文标题
树格：决策树竞争图形神经网络
TREE-G: Decision Trees Contesting Graph Neural Networks
论文作者
论文摘要
在处理表格数据时，基于决策树的模型是一个流行的选择，因为它们在这些数据类型上的高精度，易于应用和解释性属性。但是，在图形结构化数据方面，尚不清楚如何有效地应用它们，以将拓扑信息与图表顶点上可用的表格数据结合在一起。为了应对这一挑战，我们介绍了树格。 Tree-G通过引入专门用于图形数据的新型拆分功能来修改标准决策树。此拆分函数不仅包含节点特征和拓扑信息，而且还使用了一种新颖的指针机制，该机制允许分裂节点使用以前的拆分中计算的信息。因此，拆分函数适应了预测任务和手头的图形。我们分析了TROE-G的理论特性，并在多个图和顶点预测基准上进行了经验证明其优势。在这些实验中，Tree-G始终优于其他基于树的模型，并且通常优于其他图形学习算法，例如图形神经网络（GNNS）和图内核，有时是通过大的边缘。此外，可以解释和可视化树木模型及其预测
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 decision trees, by introducing a novel split function that is specialized for graph data. Not only does this split function incorporate the node features and the topological information, but it also uses a novel pointer mechanism that allows split nodes to use information computed in previous splits. Therefore, the split function adapts to the predictive task and the graph at hand. We analyze the theoretical properties of TREE-G and demonstrate its benefits empirically on multiple graph and vertex prediction benchmarks. In these experiments, TREE-G consistently outperforms other tree-based models and often outperforms other graph-learning algorithms such as Graph Neural Networks (GNNs) and Graph Kernels, sometimes by large margins. Moreover, TREE-Gs models and their predictions can be explained and visualized
