Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

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

Privacy concerns on social networks have received extensive attention in recent years. The
task of user identity linkage (UIL), which aims to identify corresponding users across different …

Predicting information diffusion helps grasp the overall preference of user interactions,
facilitating applications such as public opinion analysis and online marketing. Existing …

Predicting the popularity of online content is an important task for content recommendation,
social influence prediction and so on. Recent deep learning models generally utilize graph …

In Virtual Reality (VR), human pose estimation refers to the approximation of the location of
body parts of a real human in a physical world. During the past decades, virtual reality has a …

Existing approaches for information cascade prediction fall into three main categories:
feature-driven methods, point process-based methods, and deep learning-based methods …

Forecasting electricity demand is crucial for the management of smart grids to ensure a
secure, reliable and sustainable supply. Recently, a variant of convolutional neural …

Social network alignment is widely applied in web applications for identifying corresponding
nodes across different networks, such as linking users across two social networks. Existing …

Social media popularity forecasting has become a hot research topic in recent years. It is of
great significance in assisting public opinion monitoring and advertising placement. Time …