Detecting network communities using regularized spectral clustering algorithm
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 construc...
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| Veröffentlicht in: | The Artificial intelligence review 2014-04, Vol.41 (4), p.579-594 |
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| container_title | The Artificial intelligence review |
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| creator | Huang, Liang Li, Ruixuan Chen, Hong Gu, Xiwu Wen, Kunmei Li, Yuhua |
| description | 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. |
| doi_str_mv | 10.1007/s10462-012-9325-3 |
| format | Article |
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| subjects | Algorithms Artificial Intelligence Clustering Communities Computer Science Eigenvectors Identification Mathematical analysis Mathematical models Methods Modularity Recommender systems Similarity measures Social networks Spectra Target detection |
| title | Detecting network communities using regularized spectral clustering algorithm |
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