Fine-Grained Complexity and Conditional Hardness for Sparse Graphs
Udit Agarwal, Vijaya Ramachandran

TL;DR
This paper explores the fine-grained complexity of sparse graph problems with near-linear algorithms, introduces sparse reductions, and proposes the MWC Conjecture to understand their conditional hardness.
Contribution
It introduces the concept of sparse reductions for problems in the O(mn) class and establishes a partial order of problem hardness, along with the MWC Conjecture for directed graphs.
Findings
Sparse reductions between key graph problems are rare but impactful.
The MWC Conjecture suggests minimum cycle weight cannot be computed faster than O(mn).
Eccentricities are shown to be MWCC-hard, SETH-hard, and k-DSH-hard.
Abstract
We consider the fine-grained complexity of sparse graph problems that currently have time algorithms, where m is the number of edges and n is the number of vertices in the input graph. This class includes several important path problems on both directed and undirected graphs, including APSP, MWC (minimum weight cycle), and Eccentricities, which is the problem of computing, for each vertex in the graph, the length of a longest shortest path starting at that vertex. We introduce the notion of a sparse reduction which preserves the sparsity of graphs, and we present near linear-time sparse reductions between various pairs of graph problems in the class. Surprisingly, very few of the known nontrivial reductions between problems in the class are sparse reductions. In the directed case, our results give a partial order on a large collection of…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Advanced Graph Theory Research · Optimization and Search Problems
