Preprocessing, or data reduction, is a standard technique for simplifying and speeding up
computation. Written by a team of experts in the field, this book introduces a rapidly …

The survey is a comprehensive overview of the develo** area of parameterized
algorithms for graph modification problems. It describes state of the art in kernelization …

The goal of this textbook is twofold. First, the book serves as an introduction to the field of
parameterized algorithms and complexity accessible to graduate students and advanced …

Kernelization is a formalization of efficient preprocessing, aimed mainly at combinatorially
hard problems. Empirically, preprocessing is highly successful in practice, eg, in state-of-the …

Parameterization and approximation are two popular ways of co** with NP-hard
problems. More recently, the two have also been combined to derive many interesting …

In this paper we propose a new framework for analyzing the performance of preprocessing
algorithms. Our framework builds on the notion of kernelization from parameterized …

Graph Neural Networks (GNNs) have achieved great success in downstream applications
due to their ability to learn node representations. However, in many applications, graphs are …

A backdoor set is a set of variables of a propositional formula such that fixing the truth values
of the variables in the backdoor set moves the formula into some polynomial-time decidable …

Data reduction techniques are widely applied to deal with computationally hard problems in
real world applications. It has been a long-standing challenge to formally express the …

We continue the development of matroid-based techniques for kernelization, initiated by the
present authors [47]. We significantly extend the usefulness of matroid theory in …