Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, 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 …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a
variety of prediction tasks, from content recommendation to identifying protein functions …

To learn a sequential recommender, the existing methods typically adopt the sequence-to-
item (seq2item) training strategy, which supervises a sequence model with a user's next …

An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …

Personalized recommendation plays an important role in many online services. Substantial
research has been dedicated to learning embeddings of users and items to predict a user's …

Recent methods in sequential recommendation focus on learning an overall embedding
vector from a user's behavior sequence for the next-item recommendation. However, from …

Heterogeneous information network has been widely used to alleviate sparsity and cold start
problems in recommender systems since it can model rich context information in user-item …