Detecting local community structures in complex networks based on local degree central nodes
Detecting local community structures in complex networks based on local degree central nodes
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DOI：
10.1016/j.physa.2012.09.012
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发表时间：
2013-02-01
影响因子：
3.3
通讯作者：
Fang, Ming
中科院分区：
文献类型：
--
作者：
Chen, Qiong;Wu, Ting-Ting;Fang, Ming
在现实世界的图，如大型社交网络，网络图和生物网络中检测局部社区已经受到了很大的关注，因为从一个大型网络中获得完整的信息仍然是困难的和不现实的。在本文中，我们定义了术语局部度中心节点，其度大于或等于它的邻居节点的度。提出了一种基于局部度中心节点的局部社区检测方法。在我们的方法中，本地社区不是从给定的起始节点发现的，而是从与给定的起始节点相关联的本地度中心节点发现的。实验结果表明，局部中心节点是复杂网络中社区的关键节点，该方法检测出的局部社区具有较高的准确性。该算法能在更多节点上准确发现局部社区，是探索大型网络社区结构的有效方法。(C)2012爱思唯尔有限公司版权所有。
Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks. (C) 2012 Elsevier B.V. All rights reserved.