基于引力因子的加权网络重叠社区识别算法

Overlapping Community Recognition Algorithm of Weighted Networks Based on Gravity Factor

通过挖掘大数据来识别复杂社会网络上的社区,有利于对经济、政治、人口等方面的重要问题进行定量研究,社区的识别算法已经成为当前研究的热点问题。重点研究了重叠社区识别问题,提出了基于引力因子的加权复杂网络的重叠社区识别算法GWCR。该算法首先选取万有引力因子大的节点为中心节点,将节点与中心节点之间的引力因子作为衡量标准,并将节点归入社区引力因子大于某一阈值的社区,最后通过识别重叠节点来识别重叠社区。在3个真实网络数据集上的实验结果表明,与传统的重叠社区识别算法相比,GWCR算法划分的社区的模块度较高。

The recognition of community in complex social networks by mining big data can favor the quantitative research for economic, political and demographic problems. Community recognition algorithms have become a hot topic of current research. This paper focused on the research of overlapping community discovery, and proposed the overlapping community detection algorithm GWCR, which is based on gravity factor of weighted networks. Firstly, the GWCR algorithm selects the node with the largest gravitation factor as the center node, and uses the gravitation factor between one node and the central node as a measure. The node whose gravitation factor is larger than the threshold will be included in the community. Finally, overlapping communities are discovered by identifying overlapping nodes. Experimental resuits on three real network datasets show that, compared with conventional overlapping community detection algorithm, GWCR has higher modularity value.