Abstract
In this paper, the impact factors of in-degree and out-degree are introduced into community detection, and the directed weighted degree is used to measure the importance of the node. Based on the core nodes, a community detecting algorithm for directed and weighted networks is proposed. Then the community detection on the blog site of Sciencenet is conducted with standard structure entropy as a measure. Experimental results demonstrate that in directed and weighted networks, the proposed algorithm is efficient with shorter execution time. By comparing with the classical algorithm, the detecting results of our algorithm meet the trend of standard entropy better. It means the algorithm proposed is improved to some extent.
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© 2011 Springer-Verlag Berlin Heidelberg
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Liu, H., Qin, X., Yun, H., Wu, Y. (2011). A Community Detecting Algorithm in Directed Weighted Networks. In: Zhu, M. (eds) Electrical Engineering and Control. Lecture Notes in Electrical Engineering, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21765-4_2
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DOI: https://doi.org/10.1007/978-3-642-21765-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21764-7
Online ISBN: 978-3-642-21765-4
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