Abstract How might one``reduce''a graph? That is, generate a smaller graph that preserves
the global structure at the expense of discarding local details? There has been extensive …

Can one reduce the size of a graph without significantly altering its basic properties? The
graph reduction problem is hereby approached from the perspective of restricted spectral …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Graph sparsification aims to reduce the number of edges of a graph while maintaining its
structural properties. In this paper, we propose the first general and effective information …

In graph coarsening, one aims to produce a coarse graph of reduced size while preserving
important graph properties. However, as there is no consensus on which specific graph …

Graph Neural Networks (GNNs) have demonstrated superior performance across various
graph learning tasks but face significant computational challenges when applied to large …

The interconnectedness and interdependence of modern graphs are growing ever more
complex, causing enormous resources for processing, storage, communication, and …

Graph coarsening is a technique for solving large-scale graph problems by working on a
smaller version of the original graph, and possibly interpolating the results back to the …

As large-scale graphs become increasingly more prevalent, it poses significant
computational challenges to process, extract and analyze large graph data. Graph …

Given a statistical estimation problem where regularization is performed according to the
structure of a large, dense graph G, we consider fitting the statistical estimate using a\it …