Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …

Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …