论文标题
元节点：一种有效地学习异质图中复杂关系的简洁方法
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
论文作者
论文摘要
由于异质图的内在性质，现有消息传递了异质图的神经网络依赖于元路径或元图的概念。但是，在学习之前，需要对元路径和荟萃图进行预先配置，并高度依赖专家知识来构建它们。为了应对这一挑战，我们提出了一个新的元节点概念，用于传递信息，可以通过对相同类型的节点之间的关系进行明确建模，从而从复杂的异质图中学习丰富的关系知识，而无需任何元数据和元图。与元路径和元图不同，元节点不需要任何需要专业知识的预处理步骤。更进一步，我们提出了一个元节点消息传递方案，并将我们的方法应用于对比度学习模型。在有关节点聚类和分类任务的实验中，提出的元节点消息传递方法优于取决于元路径的最先进。我们的结果表明，无需在该领域经常使用的元路径，有效的异质图学习是可能的。
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.
