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