  Heterogeneous Information Network (HIN) is essential to study complicated
networks containing multiple edge types and node types. Meta-path, a sequence
of node types and edge types, is the core technique to embed HINs. Since
manually curating meta-paths is time-consuming, there is a pressing need to
develop automated meta-path generation approaches. Existing meta-path
generation approaches cannot fully exploit the rich textual information in
HINs, such as node names and edge type names. To address this problem, we
propose MetaFill, a text-infilling-based approach for meta-path generation. The
key idea of MetaFill is to formulate meta-path identification problem as a word
sequence infilling problem, which can be advanced by Pretrained Language Models
(PLMs). We observed the superior performance of MetaFill against existing
meta-path generation methods and graph embedding methods that do not leverage
meta-paths in both link prediction and node classification on two real-world
HIN datasets. We further demonstrated how MetaFill can accurately classify
edges in the zero-shot setting, where existing approaches cannot generate any
meta-paths. MetaFill exploits PLMs to generate meta-paths for graph embedding,
opening up new avenues for language model applications in graph analysis.
