  A social interaction (so-called higher-order event/interaction) can be
regarded as the activation of the hyperlink among the corresponding
individuals. Social interactions can be, thus, represented as higher-order
temporal networks, that record the higher-order events occurring at each time
step over time. The prediction of higher-order interactions is usually
overlooked in traditional temporal network prediction methods, where a
higher-order interaction is regarded as a set of pairwise interactions. We
propose a memory-based model that predicts the higher-order temporal network
(or events) one step ahead, based on the network observed in the past and a
baseline utilizing pair-wise temporal network prediction method. In eight
real-world networks, we find that our model consistently outperforms the
baseline. Importantly, our model reveals how past interactions of the target
hyperlink and different types of hyperlinks that overlap with the target
hyperlinks contribute to the prediction of the activation of the target link in
the future.
