Graph Reduction

Graph reduction techniques aim to simplify large graphs while preserving crucial properties, thereby improving the efficiency of graph-based computations and analyses. Current research focuses on developing robust reduction methods, such as sparsification and coarsening, that are effective across various graph types and algorithms, including those used in graph neural networks and combinatorial optimization. This work is driven by the need to handle increasingly large datasets and improve the scalability of algorithms, with applications ranging from machine learning on graph data to solving complex optimization problems. A key challenge is ensuring that reduction methods do not inadvertently compromise the accuracy or robustness of downstream analyses, particularly in the face of adversarial attacks.