  Recent self-supervised pre-training methods on Heterogeneous Information
Networks (HINs) have shown promising competitiveness over traditional
semi-supervised Heterogeneous Graph Neural Networks (HGNNs). Unfortunately,
their performance heavily depends on careful customization of various
strategies for generating high-quality positive examples and negative examples,
which notably limits their flexibility and generalization ability. In this
work, we present SHGP, a novel Self-supervised Heterogeneous Graph Pre-training
approach, which does not need to generate any positive examples or negative
examples. It consists of two modules that share the same attention-aggregation
scheme. In each iteration, the Att-LPA module produces pseudo-labels through
structural clustering, which serve as the self-supervision signals to guide the
Att-HGNN module to learn object embeddings and attention coefficients. The two
modules can effectively utilize and enhance each other, promoting the model to
learn discriminative embeddings. Extensive experiments on four real-world
datasets demonstrate the superior effectiveness of SHGP against
state-of-the-art unsupervised baselines and even semi-supervised baselines. We
release our source code at: https://github.com/kepsail/SHGP.
