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 …

Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Higher-order relations are widespread in nature, with numerous phenomena involving
complex interactions that extend beyond simple pairwise connections. As a result …

The urban metro system accommodates significant travel demand and alleviates traffic
congestion. Improving metro operational efficiency can increase the metro operator revenue …

In this work, we introduce a hypergraph representation learning framework called
Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a …

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize
urban rail transit (URT) system resource allocation. Although graph-based models have …

Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications, and thus investigation of deep learning for HOIs has become a valuable …

Graph contrastive learning (GCL) provides a new perspective to alleviate the reliance on
labeled data for graph representation learning. Recent efforts on GCL leverage various …

Cyber security has always been an important issue in the Internet of Everything topic. In the
physical layer of the Internet, specific emitter identification (SEI) technology is widely …

Accurate prediction of origin-destination (OD) demand is critical for service providers to
efficiently allocate limited resources in regions with high travel demands. However, OD …