  We are living in a world which is getting more and more interconnected and,
as physiological effect, the interaction between the entities produces more and
more information. This high throughput generation calls for techniques able to
reduce the volume of the data, but still able to preserve the carried
knowledge. Data compression and summarization techniques are one of the
possible approaches to face such problems. The aim of this thesis is to devise
a new pipeline for compressing and decompressing a graph by exploiting
Szemer\'edi's Regularity Lemma. In particular, it has been developed a
procedure called CoDec (Compression-Decompression) which is based on Alon et
al's constructive version of the Regularity Lemma. We provide an extensive
experimental evaluation to measure how robust is the framework as we both
corrupt the structures carried by the graph and add noisy edges among them. The
experimental results make us confident that our method can be effectively used
as a graph compression technique able to preserve meaningful patterns of the
original graph.
