Graph contrastive learning has been successfully applied in text classification due to its
remarkable ability for self-supervised node representation learning. However, explicit graph …

Fake news spreads at unprecedented speeds through online social media, raising many
concerns and negative impacts on a variety of domains. To control this issue, Fake News …

Graph Contrastive Learning (GCL) has achieved great success in self-supervised
representation learning throughout positive and negative pairs based on graph neural …

Text representation learning is significant as the cornerstone of natural language
processing. In recent years, graph contrastive learning (GCL) has been widely used in text …

Abstract Hierarchical Text Classification (HTC) organizes candidate labels into a
hierarchical structure and uses one or more paths within the hierarchy as the ground-truth …

Abstract Criteria Based Content Analysis (CBCA) is a forensic tool that analyzes victim
statements. It involves the categorization of victims' statements into 19 distinct criteria …

Abstract Generalized Category Discovery (GCD) task involves transferring knowledge from
labeled known categories to recognize both known and novel categories within an …

Since existing GNN methods use a fixed input graph structure for messages passing, they
cannot solve the problems of heterogeneity, over-squashing, long-range dependencies, and …

Despite the widespread use of LLMs due to their superior performance in various tasks, their
high computational costs often lead potential users to opt for the pretraining-finetuning …

Fake news detection is a significant challenge in the digital age, which has become
increasingly important with the proliferation of social media and online communication …