计算机应用研究2012，Vol.29Issue(8)：3173-3175,3.DOI:10.3969/j.issn.1001-3695.2012.08.099
基于能量函数和模块最优化的不确定图聚类
Clustering uncertain graphs through energy function and modularity optimization
摘要
Abstract
In order to indicate that the presence of uncertainty has a clustering effect can not be ignored, this paper improved a algorithm called LinLogLayout which optimized LinLog and related energy models to compute layouts, and Newman and Gir-van' s Modularity to compute clusterings and enabled it to deal with uncertain graphs. In addition, it proposed an explicit definition of uncertain graph and generated uncertain graphs subject to Zipf distribution, and then related improvements made to the algorithm in order to meet the requirements. After evaluation on both certain graphs and uncertain graphs, synthetic data-sets and real datasets, it demonstrates that the improved LinLogLayout algorithm can handle both certain and uncertain graphs well, meanwhile the results indicate that the presence of uncertainty has a clustering effect can not be ignored.关键词
不确定图/图挖掘/能量模型/模块化聚类/图聚类Key words
uncertain graph/graph mining/energy models/modularity clustering/graph clustering分类
信息技术与安全科学引用本文复制引用
丁悦,张阳,王勇,李伟卫..基于能量函数和模块最优化的不确定图聚类[J].计算机应用研究,2012,29(8):3173-3175,3.基金项目
国家自然科学基金资助项目(60873196) （60873196）
中央高校基本科研业务费专项资金资助项目(QN2009092) （QN2009092）
