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:数字信息的爆炸和人们越来越多地参与社会网络导致大量研究活动,以制定能够从互动数据中获取有意义的信息的方法。通常,互动由网络或图表中的边缘代表,这些边缘暗含地假定互动是双向和静态的。然而,现实世界的相互作用偏离了这些假设:(一) 互动可以是多路的,涉及两个以上的节点或个人(例如家庭关系、蛋白互动),以及(二) 互动可在一段时间内发生变化(例如,意见和友谊状态的改变)。虽然在动态网络设置中研究了对称互动,在静态网络中使用高压图对多路互动进行了研究,但目前没有方法可以预测动态环境中的多路互动或超尖端。现有的相关方法无法回答时间询问,例如接下来和何时会出现何种类型的互动(例如家庭关系、蛋白质互动),以及(二) 互动可以在一段时间内发生变化(例如改变观点和友谊状态) 。我们提议的模型在神经点框架中使用动态代表学习技术对节点进行研究,而多路互动已经利用静电图进行研究,在静态网络中进行了研究,但目前没有办法可以预测多路面互动或超高端点,因此,我们提出了一些实验性动态的预测,在动态的轨道上的数据利用。