On the Complexity of Community-aware Network Sparsification
Abstract
Network sparsification is the task of reducing the number of edges of a given graph while preserving some crucial graph property. In community-aware network sparsification, the preserved property concerns the subgraphs that are induced by the communities of the graph which are given as vertex subsets. This is formalized in the -Network Sparsification problem: given an edge-weighted graph , a collection of subsets of (communities), and two numbers , the question is whether there exists a spanning subgraph of with at most edges of total weight at most such that fulfills for each community . Here, we consider two graph properties : the connectivity property (Connectivity NWS) and the property of having a spanning star (Stars NWS). Since both problems are NP-hard, we study their parameterized and fine-grained complexity. We provide a tight -time running time lower bound based on the ETH for both problems, where is the number of vertices in . The lower bound holds even in the restricted case when all communities have size at most 4, is a clique, and every edge has unit weight. For the connectivity property, the unit weight case with being a clique is the well-studied problem of computing a hypergraph support with a minimum number of edges. We then study the complexity of both problems parameterized by the feedback edge number of the solution graph . For Stars NWS, we present an XP-algorithm for . This answers an open question by Korach and Stern [Disc. Appl. Math. '08] who asked for the existence of polynomial-time algorithms for . In contrast, we show for Connectivity NWS that known polynomial-time algorithms for [Korach and Stern, Math. Program. '03; Klemz et al., SWAT '14] cannot be extended by showing that Connectivity NWS is NP-hard for .
Keywords
Cite
@article{arxiv.2402.15494,
title = {On the Complexity of Community-aware Network Sparsification},
author = {Emanuel Herrendorf and Christian Komusiewicz and Nils Morawietz and Frank Sommer},
journal= {arXiv preprint arXiv:2402.15494},
year = {2024}
}