6 papers
NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
Qianren Mao, Yashuo Luo, Ziqi Qin +12
The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
Qingyun Sun, Jiayi Luo, Haonan Yuan +4
Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability un…
Robust Graph Condensation via Classification Complexity Mitigation
Jiayi Luo, Qingyun Sun, Beining Yang +5
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC…
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Haonan Yuan, Qingyun Sun, Junhua Shi +4
Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph…
DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Qingyun Sun, Jiaqi Yuan, Shan He +5
Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods…
Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation
Qianren Mao, Qili Zhang, Hanwen Hao +11
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of Large Language Models (LLMs), particularly in Questi…