基于重叠度与模块度增量的复杂网络社区识别
发布时间：2019-04-17 20:48
【摘要】：在现实网络规模不断增大的同时,其结构也越来越复杂,针对传统社区算法难以高效处理大规模复杂网络数据的问题,提出了一种基于社区重叠度和模块度增量的社区识别方法。首先根据社区节点聚集度较大的特点寻找中心节点,初步划分社区;然后将属于多个社区的重叠节点进行拆分,进而得出社区的重叠度和模块度增量;最后找出模块度增量为零的节点对,从而实现对大规模复杂社区的识别。对重叠度和模块度增量进行了分析,结果表明:所提出的算法能够有效地识别重叠社区,并具有较高的运行效率。
[Abstract]:While the scale of the real network is increasing, its structure is becoming more and more complex, aiming at the problem that the traditional community algorithm is difficult to deal with the large-scale and complex network data efficiently. This paper proposes a community identification method based on the increment of community overlap and modularity. Firstly, according to the characteristics of high concentration of community nodes, the central nodes are found and the communities are initially divided; then the overlapping nodes belonging to multiple communities are split, and then the overlap degree and modularity increment of the community are obtained. Finally, the node pairs with zero modularity increment are found to realize the identification of large-scale complex communities. The overlapping degree and modularity increment are analyzed. The results show that the proposed algorithm can effectively identify overlapping communities and has high operational efficiency.
【作者单位】： 北京信息科技大学计算机学院;
【基金】：国家自然科学基金资助项目(61671070) 网络文化与数字传播北京市重点实验室开放课题(ICDD201608)
【分类号】：O157.5;TP301.6
本文编号：2459780
[Abstract]:While the scale of the real network is increasing, its structure is becoming more and more complex, aiming at the problem that the traditional community algorithm is difficult to deal with the large-scale and complex network data efficiently. This paper proposes a community identification method based on the increment of community overlap and modularity. Firstly, according to the characteristics of high concentration of community nodes, the central nodes are found and the communities are initially divided; then the overlapping nodes belonging to multiple communities are split, and then the overlap degree and modularity increment of the community are obtained. Finally, the node pairs with zero modularity increment are found to realize the identification of large-scale complex communities. The overlapping degree and modularity increment are analyzed. The results show that the proposed algorithm can effectively identify overlapping communities and has high operational efficiency.
【作者单位】： 北京信息科技大学计算机学院;
【基金】：国家自然科学基金资助项目(61671070) 网络文化与数字传播北京市重点实验室开放课题(ICDD201608)
【分类号】：O157.5;TP301.6
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