Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
This addresses the flexibility and generalization limitations in heterogeneous graph learning for researchers and practitioners, though it appears incremental as it builds on existing self-supervised methods.
The paper tackles the problem of self-supervised pre-training on heterogeneous graphs by proposing SHGP, which eliminates the need for generating positive or negative examples, achieving superior performance over state-of-the-art unsupervised and semi-supervised baselines on four real-world datasets.
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.