  In recent years, semi-supervised graph learning with data augmentation (DA)
is currently the most commonly used and best-performing method to enhance model
robustness in sparse scenarios with few labeled samples. Differing from
homogeneous graph, DA in heterogeneous graph has greater challenges:
heterogeneity of information requires DA strategies to effectively handle
heterogeneous relations, which considers the information contribution of
different types of neighbors and edges to the target nodes. Furthermore,
over-squashing of information is caused by the negative curvature that formed
by the non-uniformity distribution and strong clustering in complex graph. To
address these challenges, this paper presents a novel method named
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
(HG-MDA). For the problem of heterogeneity of information in DA, node and
topology augmentation strategies are proposed for the characteristics of
heterogeneous graph. And meta-relation-based attention is applied as one of the
indexes for selecting augmented nodes and edges. For the problem of
over-squashing of information, triangle based edge adding and removing are
designed to alleviate the negative curvature and bring the gain of topology.
Finally, the loss function consists of the cross-entropy loss for labeled data
and the consistency regularization for unlabeled data. In order to effectively
fuse the prediction results of various DA strategies, the sharpening is used.
Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry
dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA
is applied to user identification in internet finance scenarios, helping the
business to add 30% key users, and increase loans and balances by 3.6%, 11.1%,
and 9.8%.
