  Existing message passing neural networks for heterogeneous graphs rely on the
concepts of meta-paths or meta-graphs due to the intrinsic nature of
heterogeneous graphs. However, the meta-paths and meta-graphs need to be
pre-configured before learning and are highly dependent on expert knowledge to
construct them. To tackle this challenge, we propose a novel concept of
meta-node for message passing that can learn enriched relational knowledge from
complex heterogeneous graphs without any meta-paths and meta-graphs by
explicitly modeling the relations among the same type of nodes. Unlike
meta-paths and meta-graphs, meta-nodes do not require any pre-processing steps
that require expert knowledge. Going one step further, we propose a meta-node
message passing scheme and apply our method to a contrastive learning model. In
the experiments on node clustering and classification tasks, the proposed
meta-node message passing method outperforms state-of-the-arts that depend on
meta-paths. Our results demonstrate that effective heterogeneous graph learning
is possible without the need for meta-paths that are frequently used in this
field.
