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By shifting computation from deep message passing to efficient meta-path aggregation, MetaRTL captures rich relational semantics while maintaining high efficiency.
This paper proposes Intra-Inter Relational Table Learning (InRTL), a unified framework that explicitly models dependencies both within and across relational tables, and formalizes two complementary interaction patterns: intra-table interactions and inter-table interactions.
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