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Detecting network communities using regularized spectral clustering algorithm
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DOI：10.1007/s10462-012-9325-3.png)
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Abstract
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The progressively scale of online social network leads to the difficulty of traditional algorithms on detecting communities. We introduce an efficient and fast algorithm to detect community structure in social networks. Instead of using the eigenvectors in spectral clustering algorithms, we construct a target function for detecting communities. The whole social network communities will be partitioned by this target function. We also analyze and estimate the generalization error of the algorithm. The performance of the algorithm is compared with the standard spectral clustering algorithm, which is applied to different well-known instances of social networks with a community structure, both computer generated and from the real world. The experimental results demonstrate the effectiveness of the algorithm.
Keywords:
Community detection
Graph laplacian
Eigenvector
Spectral clustering algorithm
Regularized spectral clustering algorithm
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