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

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network