Engineering Semi-streaming DFS algorithms

TL;DR

This paper experimentally analyzes semi-streaming algorithms for depth-first search, introducing heuristics that significantly improve practical performance and enable near-optimal pass efficiency in various graph models.

Contribution

The paper provides an empirical evaluation of semi-streaming DFS algorithms and proposes heuristics that enhance their practicality and efficiency, achieving near-optimal passes.

Findings

Heuristics improve state-of-the-art algorithms by 40-90%.

Almost 50% of cases require only one pass with heuristics.

Heuristics enable practical use of simpler algorithms for small space bounds.

Abstract

Depth first search is a fundamental graph problem having a wide range of applications. For a graph $G=(V,E)$ having $n$ vertices and $m$ edges, the DFS tree can be computed in $O(m+n)$ using $O(m)$ space where $m=O(n^2)$. In the streaming environment, most graph problems are studied in the semi-streaming model where several passes (preferably one) are allowed over the input, allowing $O(nk)$ local space for some $k=o(n)$. Trivially, using $O(m)$ space, DFS can be computed in one pass, and using $O(n)$ space, it can be computed in $O(n)$ passes. Khan and Mehta [STACS19] presented several algorithms allowing trade-offs between space and passes, where $O(nk)$ space results in $O(n/k)$ passes. They also empirically analyzed their algorithm to require only a few passes in practice for even $O(n)$ space. Chang et al. [STACS20] presented an alternate proof for the same and also presented $O(\sqrt{n})$ pass algorithm requiring $O(n~poly\log n)$ space with a finer trade-off between space and passes. However, their algorithm uses complex black box algorithms, making it impractical. We perform an experimental analysis of the practical semi-streaming DFS algorithms. Our analysis ranges from real graphs to random graphs (uniform and power-law). We also present several heuristics to improve the state-of-the-art algorithms and study their impact. Our heuristics improve state of the art by $40-90\%$, achieving optimal one pass in almost $40-50\%$ cases (improved from zero). In random graphs, they improve from $30-90\%$, again requiring optimal one pass for even very small values of $k$. Overall, our heuristics improved the relatively complex state-of-the-art algorithm significantly, requiring merely two passes in the worst case for random graphs. Additionally, our heuristics made the relatively simpler algorithm practically usable even for very small space bounds, which was impractical earlier.