Many events occur in the world. Some event types are stochastically excited or inhibited—in
the sense of having their probabilities elevated or decreased—by patterns in the sequence …

Large volumes of event data are becoming increasingly available in a wide variety of
applications, such as healthcare analytics, smart cities and social network analysis. The …

Capturing the occurrence dynamics is crucial to predicting which type of events will happen
next and when. A common method to do this is through Hawkes processes. To enhance …

Online detection of changes in stochastic systems, referred to as sequential change
detection or quickest change detection, is an important research topic in statistics, signal …

We propose the first multistage intervention framework that tackles fake news in social
networks by combining reinforcement learning with a point process network activity model …

The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced
sequences of discrete events. To handle complex domains with many event types, Mei et …

Social media have become an integral part of our lives, expanding our interlinking
capabilities to new levels. There is plenty to be said about their positive effects. On the other …

We consider control problems for multi-stage campaigning over social networks. The
dynamic programming framework is employed to balance the high present reward and large …

Information diffusion prediction targets on forecasting how information items spread among
a set of users. Recently, neural networks have been widely used in modeling information …

A multivariate Hawkes process is a class of marked point processes: A sample consists of a
finite set of events of unbounded random size; each event has a real-valued time and a …