On the Complexity of Fundamental Problems for DAG-Compressed Graphs

TL;DR

This paper investigates the properties and computational complexity of DAG compression for graphs, demonstrating its advantages over tree compression, extending algorithms like Kruskal's to compressed graphs, and proving the NP-hardness of finding optimal DAG compressions.

Contribution

It proves DAG compression can outperform tree compression, adapts Kruskal's algorithm for compressed graphs, and establishes NP-hardness of computing minimal DAG compressions.

Findings

DAG compression can achieve better size reduction than tree compression.

Kruskal's algorithm can be adapted to work directly on DAG compressed graphs.

Computing the minimum-size DAG compression is NP-hard, even dynamically.

Abstract

A DAG compression of a (typically dense) graph is a simple data structure that stores how vertex clusters are connected, where the clusters are described indirectly as sets of reachable sinks in a directed acyclic graph (DAG). They generalize tree compressions, where the clusters form a tree-like hierarchy, and we give the first proof that DAG compressions can achieve better compressions than tree compressions. Our interest in DAG compression stems from the fact that several simple standard algorithms, like breadth-first search on graphs, can be implemented so that they work directly on the compressed rather than on the original graph and so that, crucially, the runtime is relative to the (typically small) size of the compressed graph. We add another entry to the list of algorithms where this is possible, by showing that Kruskal's algorithm for computing minimum spanning trees can be adapted to work directly on DAG compressions. On the negative side, we answer the central open problem from previous work, namely how hard it is to compute a minimum-size DAG compression for a given graph: This is NP-hard; and this is even the case for the dynamic setting, where we must update the DAG compression optimally when a single edge is added or deleted in the input graph.