📄 中文摘要
该研究提出了一种基于假设检验的方法，用于在复杂网络中进行自动社区检测。通过构建统计模型，研究者能够有效识别网络中的社区结构，从而揭示节点之间的潜在关系。该方法不仅提高了社区检测的准确性，还能够处理大规模网络数据，适应不同类型的网络结构。实验结果表明，该算法在多个基准数据集上表现优异，具有良好的实用性和可扩展性。此项研究为网络分析和社交网络研究提供了新的工具和思路。
基于假设检验的网络自动社区检测
出处: Hypothesis Testing for Automated Community Detection in Networks
发布: 2026年3月25日
📄 English Summary
Hypothesis Testing for Automated Community Detection in Networks
This study presents a hypothesis testing-based approach for automated community detection in complex networks. By constructing statistical models, researchers can effectively identify community structures within networks, revealing potential relationships among nodes. The method enhances the accuracy of community detection and is capable of handling large-scale network data, adapting to various types of network structures. Experimental results demonstrate that the algorithm performs excellently across multiple benchmark datasets, showcasing its practicality and scalability. This research provides new tools and insights for network analysis and social network studies.