Heterogeneous network embedding (HNE) is a challenging task due to the
diverse node types and/or diverse relationships between nodes. Existing HNE
methods are typically unsupervised. To maximize the profit of utilizing the
rare and valuable supervised information in HNEs, we develop a novel Active
Heterogeneous Network Embedding (ActiveHNE) framework, which includes two
components: Discriminative Heterogeneous Network Embedding (DHNE) and Active
Query in Heterogeneous Networks (AQHN). In DHNE, we introduce a novel
semi-supervised heterogeneous network embedding method based on graph
convolutional neural network. In AQHN, we first introduce three active
selection strategies based on uncertainty and representativeness, and then
derive a batch selection method that assembles these strategies using a
multi-armed bandit mechanism. ActiveHNE aims at improving the performance of
HNE by feeding the most valuable supervision obtained by AQHN into DHNE.
Experiments on public datasets demonstrate the effectiveness of ActiveHNE and
its advantage on reducing the query cost.

本论文提出了一种基于主动学习的异构网络嵌入框架 ActiveHNE，该框架使用图卷积神经网络实现半监督的异构网络嵌入，并开发了基于不确定性和代表性的三种主动选择策略，使用多臂赌博机机制组装这些策略，以改进异构网络嵌入的性能，实验证明其具有很好的性能。