Forecasting relations between entities is paramount in the current era of data and AI. However, it is often overlooked that real-world relationships are inherently directional, involve more than two entities, and can change with time. In this paper, we provide a comprehensive solution to the problem of forecasting directional relations in a general setting, where relations are higher-order, i.e., directed hyperedges in a hypergraph. This problem has not been previously explored in the existing literature. The primary challenge in solving this problem is that the number of possible hyperedges is exponential in the number of nodes at each event time. To overcome this, we propose a sequential generative approach that segments the forecasting process into multiple stages, each contingent upon the preceding stages, thereby reducing the search space involved in predictions of hyperedges. The first stage involves a temporal point process-based node event forecasting module that identifies the subset of nodes involved in an event. The second stage is a candidate generation module that predicts hyperedge sizes and adjacency vectors for nodes observing events. The final stage is a directed hyperedge predictor that identifies the truth by searching over the set of candidate hyperedges. To validate the effectiveness of our model, we compiled five datasets and conducted an extensive empirical study to assess each downstream task. Our proposed method achieves a performance gain of 32\% and 41\% compared to the state-of-the-art pairwise and hyperedge event forecasting models, respectively, for the event type prediction.

翻译：在当今数据与人工智能时代，预测实体间关系至关重要。然而，现实世界中的关系本质上具有方向性、涉及两个以上实体且随时间动态变化的特点常被忽视。本文针对一般场景下的定向关系预测问题提出了一个完整解决方案，其中关系为高阶形式，即超图中的有向超边。该问题在现有文献中尚未被系统探讨。解决此问题的核心挑战在于：在每个事件时间点，可能的超边数量随节点数呈指数级增长。为克服此困难，我们提出了一种序列生成方法，将预测过程分解为多个阶段，每个阶段的结果依赖于前一阶段，从而缩减超边预测的搜索空间。第一阶段采用基于时序点过程的节点事件预测模块，识别参与事件的节点子集；第二阶段为候选生成模块，预测发生事件节点的超边规模与邻接向量；最终阶段通过在有向超边候选集中进行搜索，实现定向超边的精准判定。为验证模型有效性，我们构建了五个数据集并开展广泛实证研究以评估各项下游任务。在事件类型预测任务中，相较于最先进的成对关系与超边事件预测模型，本方法分别实现了32%与41%的性能提升。