Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan  recently proposed an objective func- tion 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 propose two new spectral clustering algorithms that seek to maximize Q. Experimental results indicate that the new algorithms are efficient and effective at finding both good clusterings and the appropriate number of clusters across a variety of real-world graph data sets. In addition, the spectral algorithms are much faster for large sparse graphs, scaling roughly linearly with the number of nodes n in the graph, compared to O(n2) for previous clus- tering algorithms using the Q function.
White, S., & Smyth, P. (2005). A Spectral Clustering Approach To Finding Communities in Graphs. In Proceedings of the 2005 SIAM International Conference on Data Mining (pp. 274–285). Philadelphia, PA: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972757.25