7 papers
Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
Shuai Zhen, Yifan Zhang, Yuling Wang +1
Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes (-…
Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily
Kangkang Lu, Yanhua Yu, Zhiyong Huang +1
Spectral Graph Neural Networks (SGNNs) have achieved remarkable performance in tasks such as node classification due to their ability to learn flexible filters. Typically, these fi…
LightRAG: Simple and Fast Retrieval-Augmented Generation
Zirui Guo, Lianghao Xia, Yanhua Yu +2
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant respon…
Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective
Kangkang Lu, Yanhua Yu, Zhiyong Huang +6
Graph neural networks (GNNs) have demonstrated excellent performance in semi-supervised node classification tasks. Despite this, two primary challenges persist: heterogeneity and h…
LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Tu Ao, Yanhua Yu, Yuling Wang +7
Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly o…
GraphEdit: Large Language Models for Graph Structure Learning
Zirui Guo, Lianghao Xia, Yanhua Yu +4
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural…