Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

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 …

Node classification is an important problem on graphs. While recent advances in graph
neural networks achieve promising performance, they require abundant labeled nodes for …

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

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

One important task in the study of information cascade is to predict the future recipients of a
message given its past spreading trajectory. While the network structure serves as the …

Predicting the diffusion cascades is a critical task to understand information spread on social
networks. Previous methods usually focus on the order or structure of the infected users in a …