Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unlabeled nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to distinguish one node from another). In this paper, we analyze the limitation of existing approaches in two types of classification tasks, graph classification and node classification. Inspired by our analysis, we propose two techniques, Dynamic Labeling and Preferential Dynamic Labeling, that satisfy desired properties statistically or asymptotically for each type of the task. Experimental results show that we achieve high performance in various graph-related tasks.

中文翻译：

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未标记图神经网络的动态标记

现有的图神经网络（GNN）很大程度上依赖于节点嵌入，即通过其身份，类型或内容将节点表示为向量。但是，在现实世界的应用程序（例如匿名社交网络）中广泛存在带有未标记节点的图。以前的GNN会为节点分配随机标签（这会向GNN引入伪影），或者为所有节点分配一个嵌入（无法将一个节点与另一个节点区分开）。在本文中，我们分析了现有方法在两类分类任务中的局限性：图分类和节点分类。受我们的分析启发，我们提出了两种技术，即动态标签和优先动态标签，它们可以针对每种任务在统计上或渐近上满足所需的属性。