Spectral Clustering in Social Networks

We evaluate various heuristics for hierarchical spectral clustering in large telephone call and Web graphs. Spectral clustering without additional heuristics often produces very uneven cluster sizes or low quality clusters that may consist of several disconnected components, a fact that appears to be common for several data sources but, to our knowledge, no general solution provided so far. Divide-and-Merge, a recently described postfiltering procedure may be used to eliminate bad quality branches in a binary tree hierarchy. We propose an alternate solution that enables k -way cuts in each step by immediately filtering unbalanced or low quality clusters before splitting them further. Our experiments are performed on graphs with various weight and normalization built based on call detail records and Web crawls. We measure clustering quality both by modularity as well as by the geographic and topical homogeneity of the clusters. Compared to divide-and-merge, we give more homogeneous clusters with a more desirable distribution of the cluster sizes.