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Recent years are characterized by an unprecedented quantity of available network data which are produced at an astonishing rate by an heterogeneous variety of interconnected sensors and devices. This high-throughput generation calls for the development of new effective methods to store, retrieve, understand and process massive network data. In this thesis, we tackle this challenge by introducing a framework to summarize large graphs based on Szemer\'edi's Regularity Remma (RL), which roughlyarXiv:1909.07420v2 fatcat:yconxoyh4ffezb6ahglrdbnruy