Interaction event network modeling based on temporal point process
提出一个基于时间点过程的动态模型，用于分析社交、生物等领域的交互事件网络，通过历史事件预测未来交互，并开发了网络表示学习方法，能有效监测节点间的交互率。
Interaction event networks, which consist of interaction events among a set of individuals, exist in many areas from social, biological to financial applications. The individuals on networks interact with each other for several possible reasons, such as periodic contact or reply to former interactions. Regarding these interaction events as expectations based on previous interactions is crucial for understanding the underlying network and the corresponding dynamics. Usually, any change on individuals of the network will reflect on the pattern of their interaction events. However, the causes and expressed patterns for interaction events on networks have not been properly considered in network models. This article proposes a dynamic model for interaction event networks based on the temporal point process, which aims to incorporate the impact from historical interaction events on later interaction events considering both network structure and node connections. A network representation learning method is developed to learn the interaction event processes. The proposed interaction event network model also provides a convenient representation of the rate of interaction events for any pair of sender–receiver nodes on the network and therefore facilitates monitoring such event networks by summarizing these pairwise rates. Both simulation experiments and experiments on real-world data validate the effectiveness of the proposed model and the corresponding network representation learning algorithm.