Abstract Large Language Models (LLMs) have achieved unprecedented breakthroughs in
various natural language processing domains. However, the enigmatic``black-box''nature of …

The human brain is a highly complex neurological system that has been the subject of
continuous exploration by scientists. With the help of modern neuroimaging techniques …

To deal with distribution shifts in graph data, various graph out-of-distribution (OOD)
generalization techniques have been recently proposed. These methods often employ a two …

In graph few-shot learning, few-shot node classification (FSNC) at the node-level is a
popular downstream task. Previous FSNC methods primarily rely on meta-learning or metric …

Graph contrastive learning (GCL) aims to contrast positive–negative counterparts to learn
the node embeddings, whereas graph data augmentation methods are employed to …

The success of GNNs highly relies on the accurate labeling of data. Existing methods of
ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing …

Many large-scale machine learning approaches and graph algorithms are proposed
recently to address a variety of problems in online social networks (OSNs). To evaluate and …

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to
downstream tasks with limited labeled data, sparking considerable interest among …

In the broader machine learning literature, data-generation methods demonstrate promising
results by generating additional informative training examples via augmenting sparse labels …

Traditional text classification models face challenges in handling long texts and
understanding topic transitions in dialogue scenarios, leading to suboptimal performance in …