Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

The explosive growth in fake news and its erosion to democracy, justice, and public trust has
increased the demand for fake news detection and intervention. This survey reviews and …

Cross-modal learning is essential to enable accurate fake news detection due to the fast-
growing multimodal contents in online social communities. A fundamental challenge of …

The proliferation of fake news on social media has the probability to bring an unfavorable
impact on public opinion and social development. Many efforts have been paid to develop …

A protuberant issue of the present time is that, organizations from different domains are
struggling to obtain effective solutions for detecting online-based fake news. It is quite …

The wide spread of rumors on social media has caused a negative impact on people's daily
life, leading to potential panic, fear, and mental health problems for the public. How to …

Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to
explain the predictions made by GNNs. Existing explanation methods mainly focus on post …