Kernel spectral clustering for community detection in complex networks

Rocco Langone, Carlos Alzate, Johan A. K. Suykens
2012 The 2012 International Joint Conference on Neural Networks (IJCNN)  
This paper is related to community detection in complex networks. We show the use of kernel spectral clustering for the analysis of unweighted networks. We employ the primaldual framework and make use of out-of-sample extensions. In particular, we propose a method to extract from a network a small subgraph representative for its overall community structure. We use a model selection procedure based on the modularity statistic which is novel, because modularity is commonly used only at a training
more » ... level. We demonstrate the effectiveness of our model on synthetic networks and benchmark data from real networks (power grid network and protein interaction network of yeast). Finally, we compare our model with the Nyström method, showing that our approach is better in terms of quality of the discovered partitions and needs less computation time.
doi:10.1109/ijcnn.2012.6252726 dblp:conf/ijcnn/LangoneAS12 fatcat:vtzvesp3nvetppawtxcrrntqpm