1 citations · 1 across the 6 of their papers we have counts for
7 papers
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He +5
Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment bet…
A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Di Jin, Jun Yang, Xiaobao Wang +3
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and ins…
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
Xiaobao Wang, Ruoxiao Sun, Yujun Zhang +4
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to back…
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems
Runze Li, Di Jin, Xiaobao Wang +3
Graph recommendation systems have been widely studied due to their ability to effectively capture the complex interactions between users and items. However, these systems also exhi…
KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models
Zirui Chen, Xin Wang, Zhao Li +2
Recent advances in knowledge representation learning (KRL) highlight the urgent necessity to unify symbolic knowledge graphs (KGs) with language models (LMs) for richer semantic un…
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
Yongqi Huang, Jitao Zhao, Dongxiao He +3
Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining posi…