# Talk by Andreas Loukas (EPFL) : Graph reduction by local variation

Graph reduction by local variation

Abstract:

Can we reduce the size of a graph without significantly altering its basic properties?

We will approach the graph reduction problem from the perspective of restricted similarity,

a modification of a well-known measure for graph approximation. Our choice is motivated

by the observation that restricted similarity implies strong spectral guarantees and can be

used to prove statements about certain unsupervised learning problems. The talk will then

focus on coarsening, a popular type of graph reduction. We will derive sufficient conditions

for a small graph to approximate a larger one in the sense of restricted similarity. Our findings

give rise to nearly-linear coarsening algorithms that find coarse graphs of improved quality,

often by a large margin, without sacrificing speed.

paper: https://arxiv.org/abs/1808.10650

https://scholar.google.fr/citations?hl=fr&user=-XGXJbQAAAAJ&view_op=list_works&sortby=pubdate