Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

In the burgeoning field of anomaly detection within attributed networks, traditional
methodologies often encounter the intricacies of network complexity, particularly in capturing …

Predicting traffic is the main duty of an intelligent transportation system (ITS). Precise traffic
forecasts can significantly enhance the use of public funds. However, the dynamic and …

The widespread diffusion of fake news has become a critical problem on dynamic social
media worldwide, which requires effective strategies for fake news detection to alleviate its …

In this digital world with massive information sharing, spreading misinformation is relatively
easy, but its consequences are really enormous. Detecting fake news is a critical challenge …

Abstract Heterogeneous Graph Contrastive Learning (HGCL) has attracted lots of attentions
because of eliminating the requirement of node labels. The encoders used in HGCL mainly …

Fake news can be easily propagated through social media and cause negative societal
effects. Since fake news is disinformation with malicious intent, manual fact-checking …

With the extensive technological advancements and expansion, the persistent issues
regarding the creation and rapid dissemination of fake news have become a prevalent and …

5-G/6G technology improves skeleton-based human action recognition (HAR) by delivering
ultra-low latency and high data throughput for real-time and accurate security analysis of …

Abstract Knowledge graph embedding (KGE) is a method designed to predict missing
relations between entities in a knowledge graph (KG), which has garnered much attention in …