%0 Journal Article %1 donetti_detecting_2004 %A Donetti, Luca %A Muñoz, Miguel A. %D 2004 %J Journal of Statistical Mechanics: Theory and Experiment %K analysis, clustering, community detection, eigenvalue, network spectral %N 10 %P P10012 %R 10.1088/1742-5468/2004/10/P10012 %T Detecting network communities: a new systematic and efficient algorithm %U http://iopscience.iop.org/1742-5468/2004/10/P10012 %V 2004 %X An efficient and relatively fast algorithm for the detection of communities in complex networks is introduced. The method exploits spectral properties of the graph Laplacian matrix combined with hierarchical clustering techniques, and includes a procedure for maximizing the 'modularity' of the output. Its performance is compared with that of other existing methods, as applied to different well-known instances of complex networks with a community structure, both computer generated and from the real world. Our results are, in all the cases tested, at least as good as the best ones obtained with any other methods, and faster in most of the cases than methods providing similar quality results. This converts the algorithm into a valuable computational tool for detecting and analysing communities and modular structures in complex networks.

@article{donetti_detecting_2004, abstract = {An efficient and relatively fast algorithm for the detection of communities in complex networks is introduced. The method exploits spectral properties of the graph Laplacian matrix combined with hierarchical clustering techniques, and includes a procedure for maximizing the 'modularity' of the output. Its performance is compared with that of other existing methods, as applied to different well-known instances of complex networks with a community structure, both computer generated and from the real world. Our results are, in all the cases tested, at least as good as the best ones obtained with any other methods, and faster in most of the cases than methods providing similar quality results. This converts the algorithm into a valuable computational tool for detecting and analysing communities and modular structures in complex networks.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Donetti, Luca and Muñoz, Miguel A.}, biburl = {https://www.bibsonomy.org/bibtex/2725c3201339cd5caf93f61fce3fd4a28/yourwelcome}, doi = {10.1088/1742-5468/2004/10/P10012}, interhash = {1400253e45cbc1d829b912ee880c2e69}, intrahash = {725c3201339cd5caf93f61fce3fd4a28}, issn = {1742-5468}, journal = {Journal of Statistical Mechanics: Theory and Experiment}, keywords = {analysis, clustering, community detection, eigenvalue, network spectral}, language = {en}, month = oct, number = 10, pages = {P10012}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Detecting network communities: a new systematic and efficient algorithm}, url = {http://iopscience.iop.org/1742-5468/2004/10/P10012}, urldate = {2013-12-20}, volume = 2004, year = 2004 }