Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
This addresses a bottleneck in heterogeneous graph learning for researchers and practitioners by eliminating the need for expert-designed meta-paths, though it is incremental as it builds on existing message passing frameworks.
The paper tackles the challenge of learning from heterogeneous graphs without relying on pre-configured meta-paths or meta-graphs, which require expert knowledge, by proposing a meta-node concept and message passing scheme that outperforms state-of-the-art methods in node clustering and classification tasks.
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.