Currently, objects and people can communicate to formulate various networks such as
computer networks, social networks, and protein networks. Anomalies may cause abrupt …

A new class of models for dynamic networks is proposed, called mutually exciting point
process graphs (MEG). MEG is a scalable network-wide statistical model for point processes …

Dynamic modeling and online monitoring of tensor data streams with application to passenger
flow surveillance Page 1 The Annals of Applied Statistics 2024, Vol. 18, No. 3, 1789–1814 …

Network relationships can be widely seen among entities in various fields such as social
networks, supply networks and Internet of Things (IoT). Sometimes abnormal events such as …

In this chapter, we present a review of latent position models for networks. We review the
recent literature in this area and illustrate the basic aspects and properties of this modeling …

Network sequence has been commonly used for describing the longitudinal pattern of a
dynamic system. Proper online monitoring of a network sequence is thus important for …

Industry 4.0 has emerged as an important era for process monitoring and improvement. Our
expository paper provides a historical perspective on research and practice of statistical …

As data with network structures are widely seen in diverse applications, the modeling and
monitoring of network data have drawn considerable attention in recent years. When …

Networks are resilient to internal failures or external attacks. The resiliency is often
beneficial, but there are scenarios where the collapse of a network would benefit society …

The ubiquity of relational data has motivated an extensive literature on network analysis,
and over the last two decades the latent space approach has become a popular network …