  Generative self-supervised learning (SSL), especially masked autoencoders,
has become one of the most exciting learning paradigms and has shown great
potential in handling graph data. However, real-world graphs are always
heterogeneous, which poses three critical challenges that existing methods
ignore: 1) how to capture complex graph structure? 2) how to incorporate
various node attributes? and 3) how to encode different node positions? In
light of this, we study the problem of generative SSL on heterogeneous graphs
and propose HGMAE, a novel heterogeneous graph masked autoencoder model to
address these challenges. HGMAE captures comprehensive graph information via
two innovative masking techniques and three unique training strategies. In
particular, we first develop metapath masking and adaptive attribute masking
with dynamic mask rate to enable effective and stable learning on heterogeneous
graphs. We then design several training strategies including metapath-based
edge reconstruction to adopt complex structural information, target attribute
restoration to incorporate various node attributes, and positional feature
prediction to encode node positional information. Extensive experiments
demonstrate that HGMAE outperforms both contrastive and generative
state-of-the-art baselines on several tasks across multiple datasets. Codes are
available at https://github.com/meettyj/HGMAE.
