Detecting overlapping communities based on vital nodes in complex networks
Detecting overlapping communities based on vital nodes in complex networks
复制标题
基于复杂网络中重要节点的重叠社区检测
DOI：
10.1088/1674-1056/27/10/100504
复制
发表时间：
2018-10
影响因子：
1.7
通讯作者：
Justine Eustace
中科院分区：
文献类型：
--
作者：
Xingyuan Wang;Yu Wang;Xiaomeng Qin;Rui Li;Justine Eustace
复杂网络中群落结构的检测对于理解网络结构和分析网络特性具有重要意义。然而，如何选择初始种子和确定群落数量仍然是一个问题。本文提出了一种基于关键节点和初始种子的重叠社区检测算法——基于关键节点的重叠社区检测算法（DOCBVA）。首先通过筛选的方法找到重要节点，然后通过对重要节点的预处理得到种子群落。这个过程不同于大多数现有的方法，而且速度更快。然后将种子延展。我们还采用了一个新的归因度参数来扩展种子和寻找重叠群落。最后，将对前两个步骤中未处理的剩余节点进行重新处理。在算法结束之前，社区的数量可能会发生变化。实际网络数据和人工网络数据的实验结果令人满意，证明了DOCBVA算法的优越性。
Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm (DOCBVA), an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.
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