The task of inductive link prediction in (discrete) attributed multigraphs infers missing attributed links (relations) between nodes in new test multigraphs. Traditional relational learning methods face the challenge of limited generalization to OOD test multigraphs containing both novel nodes and novel relation types not seen in training. Recently, under the only assumption that all relation types share the same structural predictive patterns (single task), Gao et al. (2023) proposed an OOD link prediction method using the theoretical concept of double exchangeability (for nodes & relation types), in contrast to the (single) exchangeability (only for nodes) used to design Graph Neural Networks (GNNs). In this work we further extend the double exchangeability concept to multi-task double exchangeability, where we define link prediction in attributed multigraphs that can have distinct and potentially conflicting predictive patterns for different sets of relation types (multiple tasks). Our empirical results on real-world datasets demonstrate that our approach can effectively generalize to entirely new relation types in test, without access to additional information, yielding significant performance improvements over existing methods.

翻译：归纳式链接预测任务旨在（离散）属性多重图中推断新测试多重图中节点之间缺失的属性化链接（关系）。传统关系学习方法难以泛化至包含训练时未见的新型节点与新型关系类型的OOD测试多重图。近期，基于所有关系类型共享相同结构性预测模式（单任务）的唯一假设，Gao等人（2023）提出了一种利用双重可交换性（针对节点与关系类型）理论概念的OOD链接预测方法，该方法区别于图神经网络（GNN）设计所采用的（单一）可交换性（仅针对节点）。本研究进一步将双重可交换性概念扩展至多任务双重可交换性，其中我们定义了属性多重图中可能针对不同关系类型集合（多任务）存在不同且潜在冲突的预测模式的链接预测。在真实数据集上的实验结果表明，我们的方法能够有效泛化至测试中完全新型的关系类型，且无需额外信息支持，相较于现有方法取得了显著性能提升。