基于特征和结构信息增强的图神经网络集成学习框架

近年来,图神经网络由于其丰富的表征和推理能力受到广泛的关注,然而,目前的研究聚焦于卷积策略和网络结构的调整以获得更高的性能,不可避免地面临单一模型局限性的约束。受到集成学习思想的启发,面向图神经网络创新性地提出一套集成学习框架(EL-GNN)。不同于常规的文本和图像数据,图数据除了特征信息外还包括了丰富的拓扑结构信息。因此,EL-GNN不仅将不同基分类器的预测结果进行融合,还在集成阶段额外补充了结构信息。此外,基于特征相似或结构邻居节点通常具有相似标签的先验假设,借助特征图重构,进一步优化集成策略,充分平衡了节点的特征和结构信息。大量实验表明,提出的集成策略取得了良好的成效,并EL-GNN在节点分类任务上显著优于现有模型。

Ensemble learning framework for graph neural network with feature and structure enhancement

Recently, graph neural networks receive widespread attention due to their rich representation and reasoning capabilities.To best knowledge, current research mainly focuses on amending the convolutional strategy and network structure for higher performance, so the performance will be inevitably constrained by the limitations of the single model.Inspired by the idea of ensemble learning, this paper innovatively proposed an ensemble learning framework for graph neural network(EL-GNN).Unlike regular text and images, graph data not only had features but also had rich topology information.Therefore, EL-GNN additionally supplemented the structure information during the ensemble stage rather than merely integrating the prediction results of independent classifiers.Besides, this paper further revised the ensemble strategy through reconstructing a feature-level similarity graph for subsequent assembling, which balanced the feature and structure information on the basis of the assumptions of those nodes with the similar feature or easy reachability of high probability to share the same labels.The comprehensive experiments indicate that the proposed ensemble strategy achieves an impressive performance and EL-GNN is superior to other off-the-shelf models on the node classification task.