Dynamic systems that consist of a set of interacting elements can be abstracted as temporal networks. Recently, higher-order patterns that involve multiple interacting nodes have been found crucial to indicate domain-specific laws of different temporal networks. This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms. Here, we propose the first model, named HIT, for full-spectrum higher-order pattern prediction in temporal hypergraphs. Particularly, we focus on predicting three types of common but important interaction patterns involving three interacting elements in temporal networks, which could be extended to even higher-order patterns. HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet. HIT could achieve significant improvement (averaged 20% AUC gain to identify the interaction type, uniformly more accurate time estimation) compared to both heuristic and other neural-network-based baselines on 5 real-world large temporal hypergraphs. Moreover, HIT provides a certain degree of interpretability by identifying the most discriminatory structural features on the temporal hypergraphs for predicting different higher-order patterns.
翻译:由一组互动元素组成的动态系统可以被抽取为时间网络。 最近, 发现涉及多个互动节点的更高顺序模式对于显示不同时间网络的域别法至关重要。 这给我们带来了为这些较高顺序模式和相关的新的学习算法设计更先进的高射线模型的挑战。 我们在这里提出了第一个模型, 名为HIT, 用于在时间超测中进行全光谱高波高波模式预测。 特别是, 我们侧重于预测三种常见但重要的互动模式, 包括时间网络中的三个互动要素, 这些互动模式可以扩展到甚至更高的顺序模式。 HIT在时间高射线上提取一个焦点三重点的结构性表示, 并用它来说明互动扩张在这种三重线中的种类、 时间和原因。 HIT可以实现显著的改进( 平均为 20% ACE 收益来识别互动类型, 统一更精确的时间估计 ), 相对于5个现实世界大型时空高射线上的超光谱和其他以神经网络为基础的基线。 此外, HIT 提供了某种更高程度的解释性度,, 确定最具有歧视性的时空结构模式。