School of Software, Xidian University, Xi'an, China.
PLoS One. 2011;6(8):e23829. doi: 10.1371/journal.pone.0023829. Epub 2011 Aug 24.
The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks.
网络中的社区结构研究在多个学科中引起了极大的兴趣。其中一个挑战是在没有整个网络全局信息的情况下，从网络中的一个起始顶点中找到局部社区。许多现有的方法往往依赖于网络属性的先验假设和预定义的参数，因此准确性较高。在本文中，我们引入了一种新的局部社区质量函数，并提出了一种快速的局部扩展算法，用于揭示大规模网络中的社区。所提出的算法可以从源顶点或覆盖整个网络的社区中检测多分辨率社区。实验结果表明，该算法在真实网络和合成网络中都具有高效和良好的性能。
