Graph neural networks (GNNs) are now the mainstream method for mining graph-structured
data and learning low-dimensional node-and graph-level embeddings to serve downstream …

Session-based travel packages recommendation aims to predict users' next click based on
their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently …

Integrating multiple online social networks (OSNs) has important implications for many
downstream social mining tasks, such as user preference modelling, recommendation, and …

Multi-view learning is an emerging field that aims to enhance learning performance by
leveraging multiple views or sources of data across various domains. By integrating …

Graph-level anomaly detection aims at capturing anomalous individual graphs in a graph
set. Due to its significance in various real-world application fields, eg, identifying rare …

Abstract In the Internet of Vehicles (IoV) based on Cellular Vehicle-to-Everything (C-V2X)
wireless communication, vehicles inform surrounding vehicles and infrastructure of their …

Anomalies often occur in real-world information networks/graphs, such as malevolent users
in online review networks and fake news in social media. When representing such …

Epilepsy is one of the most common neurological disease in the world. Researchers focus
on automatic electroencephalogram (EEG) seizure detection methods and achieve …

Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent
tools for embedding graph-structured data and finding applications across various domains …