  In network representation learning we learn how to represent heterogeneous
information networks in a low-dimensional space so as to facilitate effective
search, classification, and prediction solutions. Previous network
representation learning methods typically require sufficient task-specific
labeled data to address domain-specific problems. The trained model usually
cannot be transferred to out-of-domain datasets. We propose a self-supervised
pre-training and fine-tuning framework, PF-HIN, to capture the features of a
heterogeneous information network. Unlike traditional network representation
learning models that have to train the entire model all over again for every
downstream task and dataset, PF-HIN only needs to fine-tune the model and a
small number of extra task-specific parameters, thus improving model efficiency
and effectiveness. During pre-training, we first transform the neighborhood of
a given node into a sequence. PF-HIN is pre-trained based on two
self-supervised tasks, masked node modeling and adjacent node prediction. We
adopt deep bi-directional transformer encoders to train the model, and leverage
factorized embedding parameterization and cross-layer parameter sharing to
reduce the parameters. In the fine-tuning stage, we choose four benchmark
downstream tasks, i.e., link prediction, similarity search, node
classification, and node clustering. PF-HIN consistently and significantly
outperforms state-of-the-art alternatives on each of these tasks, on four
datasets.
