%0 Journal Article %1 White2005 %A White, Scott %A Smyth, Padhraic %D 2005 %J SIAM International Conference on Data Mining %K clustering graph master spectral %T A Spectral Clustering Approach To Finding Communities in Graphs %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.8978 %X Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan 9 recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and ...

@article{White2005, abstract = {Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan [9] recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and ...}, added-at = {2011-03-22T23:24:50.000+0100}, author = {White, Scott and Smyth, Padhraic}, biburl = {https://www.bibsonomy.org/bibtex/2a39843e9df2eed57af2ddb2781b53daf/ans}, interhash = {aa8ce357b65fa68bccd8f3bc1d9919a9}, intrahash = {a39843e9df2eed57af2ddb2781b53daf}, journal = {SIAM International Conference on Data Mining}, keywords = {clustering graph master spectral}, timestamp = {2011-03-22T23:24:51.000+0100}, title = {A Spectral Clustering Approach To Finding Communities in Graphs}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.8978}, year = 2005 }