18 citations · 29 across the 11 of their papers we have counts for
6 papers · 1 filter
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Fali Wang, Zhiwei Zhang, Xianren Zhang +11
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their profi…
Stealing Training Graphs from Graph Neural Networks
Minhua Lin, Enyan Dai, Junjie Xu +3
Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands…
HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training
Fali Wang, Tianxiang Zhao, Junjie Xu +1
Graph self-training (GST), which selects and assigns pseudo-labels to unlabeled nodes, is popular for tackling label sparsity in graphs. However, recent study on homophily graphs s…
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
Quan Li, Tianxiang Zhao, Lingwei Chen +2
Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classificatio…
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
Junjie Xu, Zongyu Wu, Minhua Lin +2
Recent progress in Graph Neural Networks (GNNs) has greatly enhanced the ability to model complex molecular structures for predicting properties. Nevertheless, molecular data encom…
Robustness Inspired Graph Backdoor Defense
Zhiwei Zhang, Minhua Lin, Junjie Xu +3
Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable…