Despite recent advances in relational learning, the task of inductive link prediction in discrete attributed multigraphs with both new nodes and new relation types in test remains an open problem. In this work we tackle this task by defining the concept of double exchangeability and its associated double-permutation equivariant graph neural network that are equivariant to permutations of both node identities and edge relations. Our neural architecture imposes a structural representation of relations that can inductively generalize from training nodes and relations to arbitrarily new test nodes and relations, without the need for adaptation or retraining, thus enabling a new direction in relational learning. Finally, we introduce a general blueprint for such double equivariant representations and empirically showcase its capability on two proposed real-world benchmarks that no existing works can perform accurately.

翻译：尽管关系学习近年来取得了进展，但在离散属性多重图中针对测试时同时出现新节点和新关系类型的归纳式链接预测任务仍是一个未解难题。本研究通过定义双重可交换性及其对应的双重置换等变图神经网络来攻克这一任务——该网络在节点身份和边关系的置换下均保持等变性。我们的神经架构对关系施加结构化的表示，使其能够从训练节点和关系归纳泛化至任意测试新节点和新关系，无需适配或重新训练，从而开辟了关系学习的新方向。最后，我们提出了此类双重等变表示的通用蓝图，并在两个现有方法无法准确处理的现实世界基准测试中经验性地展示了其能力。