Heterogeneous graphs have attracted a lot of research interests recently due
to the success for representing complex real-world systems. However, existing
methods have two pain points in embedding them into low-dimensional spaces: the
mixing of structural and semantic information, and the distributional mismatch
between data and embedding spaces. These two challenges require representation
methods to consider the global and partial data distributions while unmixing
the information. Therefore, in this paper, we propose
$\text{Dis-H}^2\text{GCN}$, a Disentangled Hyperbolic Heterogeneous Graph
Convolutional Network. On the one hand, we leverage the mutual information
minimization and discrimination maximization constraints to disentangle the
semantic features from comprehensively learned representations by independent
message propagation for each edge type, away from the pure structural features.
On the other hand, the entire model is constructed upon the hyperbolic geometry
to narrow the gap between data distributions and representing spaces. We
evaluate our proposed $\text{Dis-H}^2\text{GCN}$ on five real-world
heterogeneous graph datasets across two downstream tasks: node classification
and link prediction. The results demonstrate its superiority over
state-of-the-art methods, showcasing the effectiveness of our method in
disentangling and representing heterogeneous graph data in hyperbolic spaces.

本文提出了一种名为 Dis-HGCN 的解缰离散超几何异构图卷积网络，旨在通过独立的信息传播来解缰语义特征和结构特征，以及通过超几何结构拉近数据分布和表示空间的差距，并在诸多真实世界异构图数据集上进行验证，结果证明其在超几何空间中解缰和表示异构图数据的有效性。