Abstract
To detect communities in complex networks, we generalize the modularity density(D) to weighted variants and show how optimizing the weighted function(WD) can be formulated as a spectral clustering problem, as well as a weighted kernel k-means clustering problem.We also prove equivalence of the both clustering approaches based on WD in mathematics. Using the equivalence, we propose a new eigenvector-based kernel clustering algorithms to detecting communities in complex networks, called two-layer approach. Experimental results indicate that it have better performance comparing with either direct kernel k-means algorithm or direct spectral clustering algorithm in term of quality.
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Fu, L., Gao, L. (2013). An Eigenvector-Based Kernel Clustering Approach to Detecting Communities in Complex Networks. In: Gaol, F. (eds) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engineering, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28807-4_4
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DOI: https://doi.org/10.1007/978-3-642-28807-4_4
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-28807-4
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