Interaction event network modeling based on temporal point process. (3rd July 2022)

• Record Type: Journal Article
• Authors: Dong, Hang; Wang, Kaibo
• Is Part Of: IISE transactions. Volume 54:Number 7(2022)
• Page Start: 630
• Page End: 642
• Publication Date: 2022-07-03
• Subjects: Temporal point process -- network monitoring -- network representation learning -- interaction event -- network model; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: Https://www.tandfonline.com/doi/10.1080/24725854.2021.1906468
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: 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.