论文标题
基于网络中社区检测的相似性的有效和高效的标签初始化方法
An effective and efficient label initialization method based on similarity for community detection in networks
论文作者
论文摘要
识别网络中的集群或社区结构已成为社交网络分析的组成部分。尽管提出了许多方法，但标签传播算法（LPA）是一种流行的计算高效方法，具有线性的运行时间。但是，由于LPA的随机性，LPA在同一网络上提供了不同的社区组合。已经提出了许多改进，以消除随机性来解决这个稳定问题。本文通过提出基于链接相似性的标签初始化方法提出了对标准LPA的改进。相似性是根据两个节点之间的连接强度测量的。该方法对实际和合成措施进行了测试以分析性能。
Identifying clusters or community structures in networks has become an integral part of social network analysis. Though many methods were proposed, the label propagation algorithm (LPA) is a popular computationally efficient method with running time linear. However, the LPA provides different combination of communities on the same network due to the randomness in LPA. Many improvements have been proposed to tackle this stability problem by eliminating the randomness. This paper put forward an improvement to the standard LPA by proposing a label initialization method based on link similarity. The similarity is measured based on the connection strength between two nodes. The method is tested on real and synthetic measures to analyze the performance.
