Mixing local and global information for community detection in large networks
作者：
Highlights：
• We are investigating the emergence of a community structure in large online social networks such as Facebook.
• We propose a scalable method to maximize modularity in large networks.
• Our method uses global-level information but its scalability on large networks is comparable to that of local methods.
• Edge centralities were used to map network vertices onto points of a Euclidean space.
• Experiments on synthetic and real-world networks certify the accuracy of our method.
摘要
•We are investigating the emergence of a community structure in large online social networks such as Facebook.•We propose a scalable method to maximize modularity in large networks.•Our method uses global-level information but its scalability on large networks is comparable to that of local methods.•Edge centralities were used to map network vertices onto points of a Euclidean space.•Experiments on synthetic and real-world networks certify the accuracy of our method.
论文关键词：Complex networks,Community detection,Social networks,Social network analysis
论文评审过程：Received 26 July 2012, Revised 31 October 2012, Accepted 15 November 2012, Available online 3 April 2013.
论文官网地址：https://doi.org/10.1016/j.jcss.2013.03.012
