  Heterogeneous Graph Neural Network (HGNN) has been successfully employed in
various tasks, but we cannot accurately know the importance of different design
dimensions of HGNNs due to diverse architectures and applied scenarios.
Besides, in the research community of HGNNs, implementing and evaluating
various tasks still need much human effort. To mitigate these issues, we first
propose a unified framework covering most HGNNs, consisting of three
components: heterogeneous linear transformation, heterogeneous graph
transformation, and heterogeneous message passing layer. Then we build a
platform Space4HGNN by defining a design space for HGNNs based on the unified
framework, which offers modularized components, reproducible implementations,
and standardized evaluation for HGNNs. Finally, we conduct experiments to
analyze the effect of different designs. With the insights found, we distill a
condensed design space and verify its effectiveness.
