The stock market's ascent typically mirrors the flourishing state of the economy, whereas its
decline is often an indicator of an economic downturn. Therefore, for a long time, significant …

Training graph neural networks (GNNs) on large-scale graphs can be challenging due to the
high computational expense caused by the massive number of nodes and high-dimensional …

Abstract Knowledge graph error detection is to identify erroneous triples in knowledge
graphs that are inconsistent with objective facts in the real world. In practice, the quality of …

Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-
world applications. Nonetheless, LLMs are often criticized for their tendency to produce …

Deep neural networks are ubiquitously adopted in many applications, such as computer
vision, natural language processing, and graph analytics. However, well-trained neural …

Link prediction is a crucial task in network analysis, but it has been shown to be prone to
biased predictions, particularly when links are unfairly predicted between nodes from …

Training deep neural networks (DNNs) with large-scale datasets poses considerable
challenges due to the computational complexity stemming from the vast number of samples …