The deluge of digital information in our daily life—from user-generated content, such as
microblogs and scientific papers, to online business, such as viral marketing and advertising …

Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

Effectively predicting the size of an information cascade is critical for many applications
spanning from identifying viral marketing and fake news to precise recommendation and …

Predicting the popularity of online content on social platforms is an important task for both
researchers and practitioners. Previous methods mainly leverage demographics, temporal …

Information diffusion prediction is an important task, which studies how information items
spread among users. With the success of deep learning techniques, recurrent neural …

We explore the effects of coordinated users (ie, users characterized by an unexpected,
suspicious, or exceptional similarity) in information spreading on Twitter by quantifying the …

With the development of deep learning and other technologies, the research of information
propagation prediction has also achieved important research achievements. However, the …

Information diffusion prediction captures diffusion dynamics of online messages in social
networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral …

Online social networking platforms have drastically facilitated the phenomenon of
information cascades, making cascade prediction an important task for both researchers and …

Understanding in-network information diffusion is a fundamental problem in many
applications and one of the primary challenges is to predict the information cascade size …