摘要
发现复杂网络中的结构和特征是社区发现的一个重要任务.标签传播算法(LPA)因具有接近线性的时间复杂度,常用于快速处理大规模的社区网络.针对该算法在节点的更新顺序和标签选择策略上存在很大的随机性,严重破坏了算法的稳定性和社区划分结果的准确性.提出了一种基于节点H指数的标签传播算法,即利用节点的综合影响力改进标签传播算法的节点更新顺序和标签选择策略.实验研究表明,改进算法有效地降低了算法的随机性,提高了社区划分的稳定性和准确性.
Discovering the structure and characteristics of complex networks is an important task for community dis-covery. The label propagation algorithm (LPA) , which has a near - linear time complexity, is often used to quick-ly handle large - scale community networks. However, due to the randomness of the algorithm in the update order and label selection strategy, the stability of the algorithm and the accuracy of community partitioning results are se-riously damaged. This paper proposes a label propagation algorithm based on the H - index, which uses the node influence to improve the node update order and label selection strategy of the label propagation algorithm. Experi-mental results show that the improved algorithm can effectively reduce the randomness of the algorithm and improve the stability and accuracy of community partition.
出处
《云南民族大学学报(自然科学版)》
CAS
2017年第4期317-321,共5页
Journal of Yunnan Minzu University:Natural Sciences Edition
基金
国家自然科学基金(61540063)
国家语委科研项目(WT125-61)
关键词
社区发现
标签传播
随机性
H指数
community discovery
label propagation algorithm
stability
H - index