6 papers
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jingyun Zhang, Hao Peng, Jianxin Li +2
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated p…
Hyperbolic Continuous Structural Entropy for Hierarchical Clustering
Guangjie Zeng, Hao Peng, Angsheng Li +5
Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two prima…
Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning
Xianghua Zeng, Hao Peng, Angsheng Li +1
Diffusion-based generative methods have shown promising potential for modeling trajectories from offline reinforcement learning (RL) datasets, and hierarchical diffusion has been i…
Hierarchical Decision Making Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Dingli Su +1
Hierarchical Reinforcement Learning (HRL) is a promising approach for managing task complexity across multiple levels of abstraction and accelerating long-horizon agent exploration…
Robustness Evaluation of Graph-based News Detection Using Network Structural Information
Xianghua Zeng, Hao Peng, Angsheng Li
Although Graph Neural Networks (GNNs) have shown promising potential in fake news detection, they remain highly vulnerable to adversarial manipulations within social networks. Exis…
SetKE: Knowledge Editing for Knowledge Elements Overlap
Yifan Wei, Xiaoyan Yu, Ran Song +2
Large Language Models (LLMs) excel in tasks such as retrieval and question answering but require updates to incorporate new knowledge and reduce inaccuracies and hallucinations. Tr…