The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the interactions are pairwise and static. However, real-world interactions deviate from these assumptions: (i) interactions can be multi-way, involving more than two nodes or individuals (e.g., family relationships, protein interactions), and (ii) interactions can change over a period of time (e.g., change of opinions and friendship status). While pairwise interactions have been studied in a dynamic network setting and multi-way interactions have been studied using hypergraphs in static networks, there exists no method, at present, that can predict multi-way interactions or hyperedges in dynamic settings. Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. Our proposed model uses dynamic representation learning techniques for nodes in a neural point process framework to forecast hyperedges. We present several experimental results and set benchmark results. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.

翻译：数字信息的爆炸式增长以及人们在社交网络中日益活跃的参与，催生了大量从交互数据中提取有意义信息的研究方法。通常，交互被表示为网络或图中的边，这隐含假设了交互是成对且静态的。然而，现实世界的交互偏离了这些假设：（i）交互可以是多方的，涉及两个以上节点或个体（例如家庭关系、蛋白质相互作用）；（ii）交互可能随时间变化（例如观点和友谊状态的改变）。尽管成对交互已在动态网络设置中得到研究，而多方交互（即超边）也在静态网络的超图中被探讨，但目前尚无方法能够预测动态环境中的多方交互或超边。现有相关方法无法回答诸如"下一时刻将发生何种类型的交互"以及"它将何时发生"这类时间查询问题。本文提出了一种用于超边预测的时间点过程模型，以解决上述问题。我们提出的模型在神经点过程框架中采用动态表示学习技术来学习节点表示，从而预测超边。我们展示了多项实验结果并设定了基准结果。据我们所知，这是首个利用时间点过程预测动态网络中超边的研究工作。