Adapting the TopLeaders algorithm for dynamic social networks

作者：Gao, Wenhao; Luo, Wenjian^*; Bu, Chenyang

来源：Journal of Supercomputing, 2020, 76(10): 7883-7905.

DOI：10.1007/s11227-017-2063-1

• Dynamic social network
• Community discovery
• Leader nodes

Evolutionary community discovery is a hot research topic related to the dynamic or temporal social networks. The communities detected in a dynamic network should get reasonable partition for the current network and do not deviate drastically from the previous ones. This paper is an extended version of our previous work in Gao et al. (in: Proceedings of the 2016 international conference on big data and smart computing (BigComp), pp 53-60,2016). First, an evolutionary community discovery algorithm namedEvoLeaders, which is inspired byTopLeadersalgorithm, is proposed. Second, based onTopLeaders, an improvedTopLeadersalgorithm (i.e.,AutoLeaders) is proposed. Experiments on three classic data sets are conducted, and experimental results show that theAutoLeaderscan correctly find the number of communities and at the same time can discover reasonable communities. Third, theEvoAutoLeadersalgorithm is proposed for detecting the communities in a dynamic network. Compared with theTopLeadersalgorithm andEvoLeaders, experimental results over two real-world data sets demonstrate that theEvoAutoLeadersis more suitable for dynamic scenarios.

• 出版日期2020-10
• 单位中国科学技术大学