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This paper proposes a novel non-parametric technique for clustering networks based on their structure. Many topological measures have been introduced in the literature to characterize topological properties of networks. These measures provide meaningful information about the structural properties of a network, but many networks share similar values of a given measure. Furthermore, strong correlation between these measures occur on real-world graphs, so that using them to distinguish arbitrary graphs is difficult in practice. Although a very complicated way to represent the information and the structural properties of a graph, the graph spectrum is believed to be a signature of a graph.
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