Hawkes processes are popular for modeling correlated temporal sequences that exhibit
mutual-excitation properties. Existing approaches such as feature-enriched processes or …

Self-exciting point processes have been applied to a wide variety of applications to
understand event rates and clustering as a function of time and space. Typically, estimation …

The Hawkes process is a self-exciting Poisson point process, characterised by a conditional
intensity function. Since its introduction fifty years ago, it has been the subject of numerous …

Connectivity patterns between nodes in a computer network can be interpreted and
modelled as point processes where events in a process indicate connections being …

In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to
model clustering features in emergency calls. Gaussian kernel density functions are …

Histogram maximum likelihood estimators of semi-parametric space–time self-exciting point
process models via expectation–maximization algorithm can be biased when the …

In this chapter, we propose a temporal point process model, COEVOLVE, for such joint
dynamics, allowing the intensity of one process to be modulated by that of the other. This …

Large volumes of temporal event data, such as online check-ins and electronic records of
hospital admissions, are becoming increasingly available in a wide variety of applications …

Abstract Information diffusion is the spread of information within a network. In this thesis, we
model information diffusion as a survival process. We have adopted an existing algorithm …