UC Berkeley

Clustering in networks/graphs is an important problem with applications in the analysis of gene-gene interactions, social networks, text mining, to name a few. Spectral clustering is one of the more popular techniques for such purposes, chiefly due to its computational advantage and generality of application. The algorithm's generality arises from the fact that it is not tied to any modeling assumptions on the data, but is rooted in intuitive measures of community structure such as sparsest cut based measures (Hagen and Kahng (1992), Shi and Malik (2000), Ng. et. al (2002)). © 2014 IEEE.