6 papers
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Ruichen Xu, Jingxiang Qu, Wenhan Gao +5
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit neg…
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo +1
Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models…
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs
Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou +2
Large Language Models are now key assistants in human decision-making processes. However, a common note always seems to follow: "LLMs can make mistakes. Be careful with important i…
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
Jingxiang Qu, Wenhan Gao, Jiaxing Zhang +4
3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited i…
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation
Jiaxing Zhang, Jiayi Liu, Dongsheng Luo +2
Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models. Due to the scarcity of datasets, current methods easily suffer…
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni +6
Graph Neural Networks (GNNs) have become a building block in graph data processing, with wide applications in critical domains. The growing needs to deploy GNNs in high-stakes appl…