论文标题
凝视图形神经网络的内部逻辑，具有逻辑
A Gaze into the Internal Logic of Graph Neural Networks, with Logic
论文作者
论文摘要
图形神经网络与逻辑编程共享几种关键的关系推理机制。对其训练和评估的数据集可以看作是包含基础项的数据库事实。这使得可以通过等效逻辑程序对推理机制进行建模，以更好地理解它们如何在机器学习过程中涉及的实体之间传播信息，还可以使从给定数据集中学到的知识以及如何推广到未看到测试数据的范围。 这使我们了解了本文的关键思想：借助逻辑程序建模，鉴于它们已知的连接可能与具有相似属性的节点，因此学习从图的链接结构和其节点属性的信息内容推断出的信息流。该问题称为图节点属性预测，我们的方法将在prolog程序的帮助下模仿图形神经网络训练和推理阶段的关键信息传播步骤。 我们在OGBN-ARXIV节点属性推理基准上测试了一种方法。为了推断代表引文网络中论文的节点的类标签，我们将与每个节点相关的文本的依赖树提炼成我们用作地面原始术语的有向无环图。 加上他们对其他论文的参考集合，它们在数据库中成为事实，我们在该数据库中借助Prolog程序来理解该程序，该程序模仿图形神经网络中预测节点属性的信息传播。在此过程中，我们发明了地面项相似性关系，这些关系通过在训练集中传播来自相似节点的节点属性来帮助推断测试集中的标签，并且与图形链接结构相比，我们评估了它们的有效性。 最后，我们实施了揭示数据集固有的性能上限的说明生成器。 作为一个实际结果，我们获得了一个逻辑程序，当将机器学习算法视为时，它将在节点属性预测基准上执行接近艺术的状态。
Graph Neural Networks share with Logic Programming several key relational inference mechanisms. The datasets on which they are trained and evaluated can be seen as database facts containing ground terms. This makes possible modeling their inference mechanisms with equivalent logic programs, to better understand not just how they propagate information between the entities involved in the machine learning process but also to infer limits on what can be learned from a given dataset and how well that might generalize to unseen test data. This leads us to the key idea of this paper: modeling with the help of a logic program the information flows involved in learning to infer from the link structure of a graph and the information content of its nodes properties of new nodes, given their known connections to nodes with possibly similar properties. The problem is known as graph node property prediction and our approach will consist in emulating with help of a Prolog program the key information propagation steps of a Graph Neural Network's training and inference stages. We test our a approach on the ogbn-arxiv node property inference benchmark. To infer class labels for nodes representing papers in a citation network, we distill the dependency trees of the text associated to each node into directed acyclic graphs that we encode as ground Prolog terms. Together with the set of their references to other papers, they become facts in a database on which we reason with help of a Prolog program that mimics the information propagation in graph neural networks predicting node properties. In the process, we invent ground term similarity relations that help infer labels in the test set by propagating node properties from similar nodes in the training set and we evaluate their effectiveness in comparison with that of the graph's link structure. Finally, we implement explanation generators that unveil performance upper bounds inherent to the dataset. As a practical outcome, we obtain a logic program, that, when seen as machine learning algorithm, performs close to the state of the art on the node property prediction benchmark.
