How can we separate structural information from noise in large graphs? To address this fundamental question, we propose a graph summarization approach based on Szemer'edi's Regularity Lemma, a well-known result in graph theory, which roughly states that every graph can be approximated by the union of a small number of random-like bipartite graphs called ``regular pairs''. Hence, the Regularity Lemma provides us with a principled way to describe the essential structure of large graphs using a small amount of data. Our paper has several contributions: (i) We present our summarization algorithm which is able to reveal the main structural patterns in large graphs. (ii) We discuss how to use our summarization framework to efficiently retrieve from a database the top-$k$ graphs that are most similar to a query graph. (iii) Finally, we evaluate the noise robustness of our approach in terms of the reconstruction error and the usefulness of the summaries in addressing the graph search task.
Fiorucci, Marco (Corresponding)
|Data di pubblicazione:||2020|
|Titolo:||Separating Structure from Noise in Large Graphs Using the Regularity Lemma|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.patcog.2019.107070|
|Appare nelle tipologie:||2.1 Articolo su rivista |