论文标题
归纳节点分类的神经结构化预测
Neural Structured Prediction for Inductive Node Classification
论文作者
论文摘要
本文研究了归纳设置中的节点分类，即，旨在学习标记训练图的模型，并将其推广以推断出未标记的测试图上的节点标签。通过学习有效节点表示形式以及传统的结构化预测方法，用于建模节点标签的结构化输出，例如条件随机字段（CRFS），对这个问题进行了广泛研究，以及传统的结构化预测方法。在本文中，我们提出了一种称为结构化代理网络（SPN）的新方法，该方法结合了两全其美的优势。 SPN定义了使用GNN的CRF的灵活势能。但是，学习这样的模型并非繁琐，因为它涉及用高成本推理优化最大值游戏。受马尔可夫网络定义的关节和边际分布之间的基本连接的启发，我们建议将优化问题的近似版本作为代理，从而产生了近乎最佳的解决方案，从而使学习效率更高。在两个设置上进行的广泛实验表明，我们的方法表现优于许多竞争基线。
This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. This problem has been extensively studied with graph neural networks (GNNs) by learning effective node representations, as well as traditional structured prediction methods for modeling the structured output of node labels, e.g., conditional random fields (CRFs). In this paper, we present a new approach called the Structured Proxy Network (SPN), which combines the advantages of both worlds. SPN defines flexible potential functions of CRFs with GNNs. However, learning such a model is nontrivial as it involves optimizing a maximin game with high-cost inference. Inspired by the underlying connection between joint and marginal distributions defined by Markov networks, we propose to solve an approximate version of the optimization problem as a proxy, which yields a near-optimal solution, making learning more efficient. Extensive experiments on two settings show that our approach outperforms many competitive baselines.
