We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier. The study of this problem is instructive and corresponds to many applications such as recommendations for newly formed groups with only a few users in online social networks. To cope with this problem, we propose a novel Meta Transformed Network Embedding framework (MetaTNE), which consists of three modules: (1) A \emph{structural module} provides each node a latent representation according to the graph structure. (2) A \emph{meta-learning module} captures the relationships between the graph structure and the node labels as prior knowledge in a meta-learning manner. Additionally, we introduce an \emph{embedding transformation function} that remedies the deficiency of the straightforward use of meta-learning. Inherently, the meta-learned prior knowledge can be used to facilitate the learning of few-shot novel labels. (3) An \emph{optimization module} employs a simple yet effective scheduling strategy to train the above two modules with a balance between graph structure learning and meta-learning. Experiments on four real-world datasets show that MetaTNE brings a huge improvement over the state-of-the-art methods.

中文翻译：

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通过元转换网络嵌入对具有少镜头新标签的图进行节点分类

我们研究了具有少量新标签的图上的节点分类问题，它具有两个独特的特性：（1）图中出现了新标签；(2) 新标签只有几个用于训练分类器的代表性节点。对这个问题的研究具有指导意义，并且对应于许多应用程序，例如为在线社交网络中只有少数用户的新组推荐。为了解决这个问题，我们提出了一种新颖的元转换网络嵌入框架（MetaTNE），它由三个模块组成：（1）\emph{结构模块}根据图结构为每个节点提供潜在表示。（2）\emph{元学习模块}以元学习方式将图结构和节点标签之间的关系捕获为先验知识。此外，我们引入了一个 \emph {嵌入转换函数}，它弥补了直接使用元学习的不足。本质上，元学习的先验知识可用于促进小样本小说标签的学习。(3) \emph{optimization module} 采用简单而有效的调度策略来训练上述两个模块，并在图结构学习和元学习之间取得平衡。在四个真实世界数据集上的实验表明，MetaTNE 比最先进的方法带来了巨大的改进。(3) \emph{optimization module} 采用简单而有效的调度策略来训练上述两个模块，并在图结构学习和元学习之间取得平衡。在四个真实世界数据集上的实验表明，MetaTNE 比最先进的方法带来了巨大的改进。(3) \emph{optimization module} 采用简单而有效的调度策略来训练上述两个模块，并在图结构学习和元学习之间取得平衡。在四个真实世界数据集上的实验表明，MetaTNE 比最先进的方法带来了巨大的改进。