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 …

Studying information diffusion in SNS (Social Networks Service) has remarkable
significance in both academia and industry. Theoretically, it boosts the development of other …

The problem of community-level information pathway prediction (CLIPP) aims at predicting
the transmission trajectory of content across online communities. A successful solution to …

Predicting information cascade popularity is a fundamental problem in social networks.
Capturing temporal attributes and cascade role information (eg, cascade graphs 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 …

Abstract Information cascade size prediction is one of the primary challenges for
understanding the diffusion of information. Traditional feature‐based methods heavily rely …

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 …

Supervised learning, while prevalent for information cascade modeling, often requires
abundant labeled data in training, and the trained model is not easy to generalize across …

Accurately predicting the popularity of micro-videos is crucial for real-world applications
such as recommender systems and identifying viral marketing opportunities. Existing …

Popularity prediction of online content over social media platforms is a valuable and
challenging issue, the core of which lies in how to capture predictive factors from available …