Change point analysis of events in social networks: An online convex optimization approach

Abstract

Nowadays, online social networks play a crucial role in shaping human communication in various life activities. Social Network Analysis (SNA) provides valuable insights for businesses, authorities, and platform owners. One of the challenging tasks in SNA is detecting sequential change points in observed events in social networks when the parameters of statistical distribution of post-change networks are unknown. This challenging problem is particularly prominent in various real-world network systems, especially when the events in the networks can be modeled through a Hawkes process. Identifying change points in the stream of social network data, where the underlying statistical properties undergo significant changes, necessitates the development of adaptive online algorithms. Additionally, in cases where the use of maximum likelihood estimators is impractical or when no exact recursive function for likelihood is available, addressing this issue becomes more complex. This paper proposes likelihood estimators using online convex optimization methods, incorporating the adaptive moment estimation (ADAM) algorithm. The proposed method is seamlessly integrated into the sequential anomaly detection procedure for events in social networks. Experimental results on monitoring time between events demonstrate lower Expected Delay Detection (EDD), indicating the superiority of the proposed algorithm in both synthetic and real-world datasets such as Facebook and contact networks of individuals causing disease transmission. The proposed robust solution provides an efficient practical tool in situations where traditional methods face limitations in swift detection with high accuracy.