A fast and reasonable method for community detection with adjustable extent of overlapping

Zhihao Wu, Youfang Lin, Huaiyu Wan, Sheng-Feng Tian
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引用次数: 15

Abstract

Communities exist in complex networks of different areas, and in some cases they may overlap between each other. Community detection is a good way to understand the structure, function and evolution of complex networks. There have been some methods to find disjoint or overlapping communities. While most of these methods only fit one single situation, disjoint or overlapping. In our opinion, it is unreasonable to find disjoint communities on a network with clear overlap or to find overlapping communities on a network without any visible overlapping node. In this paper, we propose a link partition based method which can find communities with adjustable extent of overlapping according to backgrounds of specific applications or personal preferences. Experimental results on some real-world networks show that our method can find reasonable communities with adjustable extent of overlapping, and is suitable for networks with high densities and large scales.
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一种重叠度可调的快速合理的群落检测方法
社区存在于不同地区的复杂网络中,在某些情况下,它们可能相互重叠。社区检测是了解复杂网络结构、功能和演化的有效途径。有一些方法可以找到不相交或重叠的群落。而这些方法大多只适用于一种情况,不相交或重叠。我们认为,在重叠明显的网络上寻找不相交的社区,或者在没有可见重叠节点的网络上寻找重叠的社区,都是不合理的。本文提出了一种基于链接划分的方法,该方法可以根据特定应用背景或个人偏好找到重叠程度可调的社区。在一些真实网络上的实验结果表明,该方法可以找到重叠程度可调的合理社区,适用于高密度、大尺度的网络。
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