Dynamic multi-relational graphs are an expressive relational representation for data enclosing entities and relations of different types, and where relationships are allowed to vary in time. Addressing predictive tasks over such data requires the ability to find structure embeddings that capture the diversity of the relationships involved, as well as their dynamic evolution. In this work, we establish a novel class of challenging tasks for dynamic multi-relational graphs involving out-of-domain link prediction, where the relationship being predicted is not available in the input graph. We then introduce a novel Graph Neural Network model, named GOOD, designed specifically to tackle the out-of-domain generalization problem. GOOD introduces a novel design concept for multi-relation embedding aggregation, based on the idea that good representations are such when it is possible to disentangle the mixing proportions of the different relational embeddings that have produced it. We also propose five benchmarks based on two retail domains, where we show that GOOD can effectively generalize predictions out of known relationship types and achieve state-of-the-art results. Most importantly, we provide insights into problems where out-of-domain prediction might be preferred to an in-domain formulation, that is, where the relationship to be predicted has very few positive examples.

翻译：动态多关系图是一种富有表现力的关系表示形式，用于包含不同类型实体和关系的数据，且这些关系允许随时间变化。对此类数据进行预测任务需要能够捕捉所涉及关系的多样性及其动态演进的结构嵌入。在本文中，我们为动态多关系图建立了一类新颖的挑战性任务——域外链接预测，其中待预测的关系在输入图中不存在。随后，我们引入了一种名为GOOD的新型图神经网络模型，专门设计用于解决域外泛化问题。GOOD引入了一种新颖的多关系嵌入聚合设计理念，其核心思想在于：当能够解缠生成良好表示的不同关系嵌入的混合比例时，该表示才是有效的。我们还基于两个零售领域提出了五个基准测试，结果表明GOOD能够有效泛化至已知关系类型之外的预测，并达到最先进水平。最重要的是，我们深入探讨了在何种问题中域外预测可能优于域内公式化——即当待预测关系仅有极少量正例时。