论文标题
签名：自动图神经网络
AutoGraph: Automated Graph Neural Network
论文作者
论文摘要
图在许多应用中起着重要的作用。最近，图形神经网络（GNN）在图形分析任务中取得了有希望的结果。已经提出了一些最先进的GNN模型，例如图形卷积网络（GCN），图形注意网络（GATS）等。尽管取得了这些成功，但大多数GNN仅具有浅层结构。这会导致GNN的低表达能力。为了充分利用深神经网络的力量，最近提出了一些深度GNN。但是，深GNN的设计需要重要的建筑工程。在这项工作中，我们提出了一种自动化深度GNNS设计的方法。在我们提出的方法中，我们在GNNS搜索空间中添加了一种新型的跳过连接，以鼓励功能再利用并减轻消失的梯度问题。我们还允许我们的进化算法在进化过程中增加GNN的层，以产生更深的网络。我们在图表节点分类任务中评估我们的方法。实验表明，我们方法生成的GNN可以在Cora，Citeseer，PubMed和PPI数据集中获得最先进的结果。
Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs. To fully utilize the power of the deep neural network, some deep GNNs have been proposed recently. However, the design of deep GNNs requires significant architecture engineering. In this work, we propose a method to automate the deep GNNs design. In our proposed method, we add a new type of skip connection to the GNNs search space to encourage feature reuse and alleviate the vanishing gradient problem. We also allow our evolutionary algorithm to increase the layers of GNNs during the evolution to generate deeper networks. We evaluate our method in the graph node classification task. The experiments show that the GNNs generated by our method can obtain state-of-the-art results in Cora, Citeseer, Pubmed and PPI datasets.
