Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data. However, existing graph based applications commonly lack annotated data. GNNs are required to learn latent patterns from a limited amount of training data to perform inferences on a vast amount of test data. The increased complexity of GNNs, as well as a single point of model parameter initialization, usually lead to overfitting and sub-optimal performance. In addition, it is known that GNNs are vulnerable to adversarial attacks. In this paper, we push one step forward on the ensemble learning of GNNs with improved accuracy, generalization, and adversarial robustness. Following the principles of stochastic modeling, we propose a new method called GNN-Ensemble to construct an ensemble of random decision graph neural networks whose capacity can be arbitrarily expanded for improvement in performance. The essence of the method is to build multiple GNNs in randomly selected substructures in the topological space and subfeatures in the feature space, and then combine them for final decision making. These GNNs in different substructure and subfeature spaces generalize their classification in complementary ways. Consequently, their combined classification performance can be improved and overfitting on the training data can be effectively reduced. In the meantime, we show that GNN-Ensemble can significantly improve the adversarial robustness against attacks on GNNs.

翻译：图神经网络（GNN）在图形结构数据中得到了广泛应用。然而，现有的基于图的应用通常缺乏标注数据。GNN需要从有限量的训练数据中学习潜在模式，以对大量测试数据进行推理。GNN复杂性的增加以及单一模型参数初始化点的问题，通常会导致过拟合和次优性能。此外，已知GNN容易受到对抗性攻击。在本文中，我们在GNN的集成学习方面向前推进了一步，提高了准确性、泛化能力和对抗鲁棒性。遵循随机建模的原则，我们提出了一种称为GNN-Ensemble的新方法，用于构建随机决策图神经网络的集成，其容量可以任意扩展以提升性能。该方法的实质是在拓扑空间的随机选择子结构中和特征空间的子特征中构建多个GNN，然后将它们组合以进行最终决策。这些位于不同子结构和子特征空间中的GNN以互补的方式泛化其分类。因此，它们的组合分类性能可以得到提升，并且对训练数据的过拟合可以有效减少。同时，我们表明GNN-Ensemble能够显著提高针对GNN攻击的对抗鲁棒性。