Community detection from signed networks
|Community detection from signed networks|
|Author(s)||Sugihara T., Liu X., Murata T.|
|Published in||Transactions of the Japanese Society for Artificial Intelligence|
|Keyword(s)||Community detection, Modularity, Signed network (Extra: Community detection, Community structures, Conventional detection, Link mining, Modularity, Wikipedia, Computer aided network analysis, Websites, Population dynamics)|
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Community detection from signed networks is a 2013 journal article written in English by Sugihara T., Liu X., Murata T. and published in Transactions of the Japanese Society for Artificial Intelligence.
Many real-world complex systems can be modeled as networks, and most of them exhibit community structures. Community detection from networks is one of the important topics in link mining. In order to evaluate the goodness of detected communities, Newman modularity is widely used. In real world, however, many complex systems can be modeled as signed networks composed of positive and negative edges. Community detection from signed networks is not an easy task, because the conventional detection methods for normal networks cannot be applied directly. In this paper, we extend Newman modularity for signed networks. We also propose a method for optimizing our modularity, which is an efficient hierarchical agglomeration algorithm for detecting communities from signed networks. Our method enables us to detect communities from large scale real-world signed networks which represent relationship between users on websites such as Wikipedia, Slashdot and Epinions.
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