Abstract Continuous-Time Dynamic Graph (CTDG) methods have shown their superior
ability in learning representations for dynamic graph-structured data, the methods split the …

Information cascade popularity prediction is an important problem in social network content
diffusion analysis. Various facets have been investigated (eg, diffusion structures and …

The rapid spread of diverse information on online social platforms has prompted both
academia and industry to realize the importance of predicting content popularity, which …

Popularity prediction, which aims to predict the diffusion size of a information cascade, plays
a crucial role in understanding the diffusion dynamics and enabling various applications in …

Information popularity prediction is important yet challenging in various domains, including
viral marketing and news recommendations. The key to accurately predicting information …

In today's digital age, conspiracies and information campaigns can emerge rapidly and
erode social and democratic cohesion. While recent deep learning approaches have made …

Temporal link prediction, aiming at predicting future interactions among entities based on
historical interactions, is crucial for a series of real-world applications. Although previous …

Popularity prediction for information cascades has significant applications across various
domains, including opinion monitoring and advertising recommendations. While most …

Predicting the size of message cascades is critical in various applications, such as online
advertising and early detection of rumors. However, most existing deep learning approaches …

Information cascade popularity prediction is critical for many applications, including but not
limited to identifying fake news and accurate recommendations. Traditional feature-based …