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.

翻译：近期，基于自监督预训练方法的信息异构网络（HINs）展现出优于传统半监督异构图神经网络（HGNNs）的竞争力。然而，这些方法的性能高度依赖于对生成高质量正样本与负样本策略的精细定制，这显著限制了其灵活性与泛化能力。本文提出SHGP——一种新型自监督异构图预训练方法，该方法无需生成任何正样本或负样本。SHGP包含两个共享相同注意力聚合机制的模块：在每次迭代中，Att-LPA模块通过结构聚类生成伪标签，这些伪标签作为自监督信号指导Att-HGNN模块学习对象嵌入与注意力系数。两个模块能够有效协同增强，推动模型学习判别性嵌入。在四个真实数据集上的广泛实验表明，SHGP在效果上显著优于最先进的无监督基线方法，甚至优于部分半监督基线方法。我们已开源实现代码：https://github.com/kepsail/SHGP。