  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.
