Abstract
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 networks. 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