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 …

Quantitative trading and investment decision making are intricate financial tasks that rely on
accurate stock selection. Despite advances in deep learning that have made significant …

With the advent of big data and the information age, the data magnitude of various complex
networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary …

Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …

With the growing number of Location-Based Social Networks, privacy-preserving point-of-
interest (POI) recommendation has become a critical challenge when helping users discover …

Graph neural networks (GNNs) have been widely used for graph structure learning and
achieved excellent performance in tasks such as node classification and link prediction …

Abstract Recently, Graph-based Few-Shot Learning (FSL) methods exhibit good
generalization by mining relations among few samples with Graph Neural Networks …

Traffic flow prediction is crucial for intelligent transportation system, such as traffic
management, congestion alleviation and public risk assessment. Recently, attention …

Friend recommendation from user trajectory is a vital real-world application of location-
based social networks (LBSN) services. Previous statistical analysis indicated that social …

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 …