Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios, and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks.

In this paper, we design a meta-learning framework, called META-HIN, for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous GNN module is used as the base model to express the features of subgraphs. We also design a GAN-based contrastive learning module that is able to exploit unsupervised information of the subgraphs.

In our experiments, we fuse several datasets from multiple domains to verify META-HIN’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.

异构信息网络 (HIN) 是许多特定领域检索和推荐场景以及对话环境中的关键资源。当前挖掘图数据的方法通常依赖于丰富的监督信息。然而，对于新任务来说，用于图学习的监督信号往往很少，并且只有少数标记节点可用。元学习机制能够利用可适应新任务的先验知识。

在本文中，我们设计了一个元学习框架，称为梅塔欣，用于 HIN 上的小样本学习问题。据我们所知，我们是最早设计一个统一框架来实现 HIN 的小样本学习并促进跨不同图域的不同下游任务的人之一。与大多数以前的模型不同，大多数模型专注于单个图表上的单个任务，梅塔欣能够处理跨多个图的不同任务（以节点分类、链接预测和异常检测为例）。对子图进行采样以构建支持和查询集。在被元学习模块处理之前，子图通过结构模块进行建模以捕获结构特征。然后，使用异构GNN模块作为基础模型来表达子图的特征。我们还设计了一个基于 GAN 的对比学习模块，能够利用子图的无监督信息。

在我们的实验中，我们融合了来自多个域的多个数据集来验证梅塔欣在多图场景中具有广泛的适用性。梅塔欣在我们考虑的每项任务和所有数据集上，其性能始终显着优于最先进的替代方案。