论文标题
具有高阶依赖性图的深层合奏
Deep Ensembles for Graphs with Higher-order Dependencies
论文作者
论文摘要
图形神经网络（GNN）继续在许多图形学习任务上实现最先进的性能，但要依靠以下假设：给定的图是真实邻域结构的足够近似。当系统包含高阶顺序依赖性时，我们表明，传统图表表示每个节点的邻域的趋势会导致现有的GNN概括较差。为了解决这个问题，我们提出了一个新颖的深图集合（DGE），该集合（DGE）通过在高阶网络结构中训练同一节点的不同邻域子空间来捕获社区差异。我们表明，DGE在六个现实世界中的六个现实世界数据集上始终胜过现有的GNN，即使在相似的参数预算下，也具有已知高阶依赖性的六个真实数据集。我们证明，学习多样而准确的基础分类器对DGE的成功至关重要，并讨论了这些发现对GNNS合奏的未来工作的含义。
Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. When a system contains higher-order sequential dependencies, we show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node within a higher-order network structure. We show that DGE consistently outperforms existing GNNs on semisupervised and supervised tasks on six real-world data sets with known higher-order dependencies, even under a similar parameter budget. We demonstrate that learning diverse and accurate base classifiers is central to DGE's success, and discuss the implications of these findings for future work on ensembles of GNNs.
