Clustering uncertain graphs through energy function and modularity optimization
基于能量函数和模块最优化的不确定图聚类

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 Girvan'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 datasets 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.