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insight - Graph Representation Learning
설명력을 유지하는 증강 기법을 활용한 준지도 학습 기반 그래프 표현 학습
본 논문에서는 그래프 증강 기법이 그래프의 핵심 의미 정보를 유지하면서도 그래프 표현 학습에 필요한 변형을 주입해야 한다는 점을 강조하며, 설명 가능한 인공지능(XAI) 기법을 활용하여 의미 정보를 보존하는 그래프 증강 기법인 EPA(Explanation-Preserving Augmentation)를 제안합니다.
LangGSL: A Robust Framework for Graph Representation Learning by Integrating Large Language Models and Graph Structure Learning
Integrating Large Language Models (LLMs) and Graph Structure Learning Models (GSLMs) significantly improves the robustness and accuracy of graph representation learning, especially in noisy or incomplete graph scenarios.
Can Large Language Models be Soft Prompted for Graph Learning Tasks?
Large Language Models can effectively comprehend graph information through soft prompts, as demonstrated by the GraphPrompter framework.
Simplified PCNet with Robustness: Enhancing Graph Neural Networks for Real-World Data
Enhancing the robustness and efficiency of Simplified PCNet for graph representation learning.
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