The ability to model the information diffusion process and predict its size is crucial to
understanding information propagation mechanism and is useful for many applications such …

In recommendation systems, utilizing the user interaction history as sequential information
has resulted in great performance improvement. However, in many online services, user …

Social networks represent complex ecosystems where the interactions between users or
groups play a pivotal role in information dissemination, opinion formation, and social …

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

Hawkes processes are point process models that have been used to capture self-excitatory
behavior in social interactions, neural activity, earthquakes and viral epidemics. They can …

Recently proposed encoder-decoder structures for modeling Hawkes processes use
transformer-inspired architectures, which encode the history of events via embeddings and …

Marked temporal point process models (MTPPs) aim to model event sequences and event
markers (associated features) in continuous time. These models have been applied to …

Temporal Point Processes (TPPs), especially Hawkes Process are commonly used for
modeling asynchronous event sequences data such as financial transactions and user …

Precipitation has a great impact on people's lives and social and economic development, so
researchers have conducted a lot of research on accurate precipitation prediction. However …

Real-world data often exhibits sequential dependence, across diverse domains such as
human behavior, medicine, finance, and climate modeling. Probabilistic methods capture …