7 papers
Empowering GNNs for Domain Adaptation via Denoising Target Graph
Haiyang Yu, Meng-Chieh Lee, Xiang song +2
We explore the node classification task in the context of graph domain adaptation, which uses both source and target graph structures along with source labels to enhance the genera…
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering
Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis +6
Knowledge graph question answering (KGQA) presents significant challenges due to the structural and semantic variations across input graphs. Existing works rely on Large Language M…
Spectro-Riemannian Graph Neural Networks
Karish Grover, Haiyang Yu, Xiang Song +4
Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic…
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases
Meng-Chieh Lee, Qi Zhu, Costas Mavromatis +5
Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? R…
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Shichang Zhang, Da Zheng, Jiani Zhang +6
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machi…
GraphStorm: all-in-one graph machine learning framework for industry applications
Da Zheng, Xiang Song, Qi Zhu +13
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challe…
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Qi Zhu, Da Zheng, Xiang Song +4
Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) hav…