کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
975462 | 1480166 | 2014 | 14 صفحه PDF | دانلود رایگان |
• We propose a fast local expansion algorithm for community detection named LSE.
• It uses quality function that enables detecting communities independently of their sizes that can be of different densities.
• The proposed algorithm can detect multiresolution community from a source vertex.
• The experimental results verify that LSE can uncover rich information on networks.
In complex networks such as computer and information networks, social networks or biological networks a community structure is a common and important property. Community detection in complex networks has attracted a lot of attention in recent years. Community detection is the problem of finding closely related groups within a network. Modularity optimisation is a widely accepted method for community detection. It has been shown that the modularity optimisation has a resolution limit because it is unable to detect communities with sizes smaller than a certain number of vertices defined with network size. In this paper we propose a metric for describing community structures that enables community detection better than other metrics. We present a fast local expansion algorithm for community detection. The proposed algorithm provides online multiresolution community detection from a source vertex. Experimental results show that the proposed algorithm is efficient in both real-world and synthetic networks.
Journal: Physica A: Statistical Mechanics and its Applications - Volume 407, 1 August 2014, Pages 380–393