activity
20232025
most citedEffective Exploration Based on the Structural Information Principles

3 citations · 5 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

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…

cs.SI2025

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…

cs.CL2025

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…

cs.LG2024★ 3 cited

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…

cs.SI2024★ 1 cited

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…

cs.LG2024

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…

cs.LG2023

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…

cs.SI2023★ 1 cited

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…