Individuals or companies in a large social or financial network often display rather
heterogeneous behaviors for various reasons. In this work, we propose a network vector …

Hawkes processes are a particularly interesting class of stochastic point processes that were
introduced in the early seventies by Alan Hawkes, notably to model the occurrence of …

Network-linked data, where multivariate observations are interconnected by a network, are
becoming increasingly prevalent in fields such as sociology and biology. These data often …

While the Vector Autoregression (VAR) model has received extensive attention for modelling
complex time series, quantile VAR analysis remains relatively underexplored for high …

Irregular and asynchronous event sequences are prevalent in many domains, such as social
media, finance, and healthcare. Traditional temporal point processes (TPPs), like Hawkes …

Sequential data mining is ubiquitous in various scenarios. Modeling event sequence and
predicting event occurrence is of vital importance in sequential data mining, and Temporal …

We consider network autoregressive models for count data with a non‐random
neighborhood structure. The main methodological contribution is the development of …

Modern neural recording techniques allow neuroscientists to obtain spiking activity from
many hundreds of neurons simultaneously over long time periods, and new statistical …

Multi-relational networks among entities are frequently observed in the era of big data.
Quantifying the effects of multiple networks have attracted significant research interest …

Clustering of event stream data is of great importance in many application scenarios,
including but not limited to, e-commerce, electronic health, online testing, mobile music …