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:在这项工作中,我们介绍SHGP,这是一个新的自我监督的超异性图示预选方法,不需要产生任何积极的例子或消极的例子。它由两个模块组成,它们都拥有相同的关注聚合计划。在每次迭代中,Att-LPA模块通过结构集群生成假标签,作为自我监督信号,用以指导Att-HGNN模块学习物体嵌入和关注系数。这两个模块可以有效地利用和相互加强,促进学习歧视嵌入模式。四个真实世界数据集的广泛实验显示SHGP对州-艺术基础值/半监督源码/ODGP/OD/ODUGS。