4 papers
Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Hongtao Wang, Renchi Yang, Haoran Zheng +1
Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in data management and mining. Ho…
SliceGX: Layer-wise GNN Explanation with Model-slicing
Tingting Zhu, Tingyang Chen, Yinghui Wu +2
Ensuring the trustworthiness of graph neural networks (GNNs), which are often treated as black-box models, requires effective explanation techniques. Existing GNN explanations typi…
In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration
Jiajie Fu, Haitong Tang, Arijit Khan +3
Entity Resolution (ER) is a fundamental data quality improvement task that identifies and links records referring to the same real-world entity. Traditional ER approaches often rel…
Graph Data Management and Graph Machine Learning: Synergies and Opportunities
Arijit Khan, Xiangyu Ke, Yinghui Wu
The ubiquity of machine learning, particularly deep learning, applied to graphs is evident in applications ranging from cheminformatics (drug discovery) and bioinformatics (protein…