Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs

• URL: http://arxiv.org/abs/2210.14480v1
• Date: Wed, 26 Oct 2022 05:04:29 GMT
• Title: Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
• Authors: Jiwoong Park, Jisu Jeong, Kyungmin Kim, Jin Young Choi
• Abstract summary: 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. Unlike meta-paths and meta-graphs, meta-nodes do not require any pre-processing steps that require expert knowledge. 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.
• Score: 18.65171129524357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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