Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Abstract Graph Neural Networks (GNNs) have shown promising results on a broad spectrum
of applications. Most empirical studies of GNNs directly take the observed graph as input …

The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and
modeling graph-structured data. In recent years, GNNs have gained significant attention in …

Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it
is susceptible to low-quality and unreliable structure, which has been a norm rather than an …

The rapid development of information technology such as the Internet of Things, Big Data,
artificial intelligence, and blockchain has changed the transaction mode of the financial …

Graph mining tasks arise from many different application domains, including social
networks, biological networks, transportation, and E-commerce, which have been receiving …

Node classification is a substantial problem in graph-based fraud detection. Many existing
works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising …