74 citations · 81 across the 11 of their papers we have counts for
12 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…
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…
Effective Exploration Based on the Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
Traditional information theory provides a valuable foundation for Reinforcement Learning, particularly through representation learning and entropy maximization for agent exploratio…
Multi-Relational Structural Entropy
Yuwei Cao, Hao Peng, Angsheng Li +3
Structural Entropy (SE) measures the structural information contained in a graph. Minimizing or maximizing SE helps to reveal or obscure the intrinsic structural patterns underlyin…
Unsupervised Social Bot Detection via Structural Information Theory
Hao Peng, Jingyun Zhang, Xiang Huang +4
Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The curre…
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…
Semi-Supervised Clustering via Structural Entropy with Different Constraints
Guangjie Zeng, Hao Peng, Angsheng Li +4
Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Desp…
Adversarial Socialbots Modeling Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socia…
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Guangjie Zeng, Hao Peng, Angsheng Li +4
Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and exist…