Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Recently," pre-training and fine-tuning''has been adopted as a standard workflow for many
graph tasks since it can take general graph knowledge to relieve the lack of graph …

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve
state-of-the-art in many critical tasks including node classification and link prediction …

Modern sociology has profoundly uncovered many convincing social criteria for behavioral
analysis. Unfortunately, many of them are too subjective to be measured and very …

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and
higher-order interactions for recommender systems. However, compared with traditional …

User purchasing prediction with multi-behavior information remains a challenging problem
for current recommendation systems. Various methods have been proposed to address it via …

The self-supervised graph representation learning has achieved much success in recent
web based research and applications, such as recommendation system, social networks …

Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One
of the important signals that these platforms rely upon is the click-through rate (CTR) …

With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship
from online social relations and offline trajectory data is of great value to improve the …

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently,
a new paradigm" pre-train, prompt" has shown promising results in adapting GNNs to …