Hierarchical Fuzzy Spectral Clustering in Social Networks Using Spectral Characterization

An important aspect of community analysis is not only determining the communities within the network, but also sub-communities and hierarchies. We present an approach for finding hierarchies in social networks that uses work from random matrix theory to estimate the number of clusters. The method analyzes the spectral fingerprint of the network to determine the level of hier-archy in the network. Using this information to inform the choice of clusters, the network is broken into suc-cessively smaller communities that are attached to their parents via Jaccard similarity. The efficacy of the ap-proach is examined on two well known real world social networks as well as a political social network derived from campaign finance data