On the External Validity of Average-Case Analyses of Graph Algorithms

TL;DR

This study systematically examines whether average-case analyses of graph algorithms on simple models are externally valid for real-world networks, finding that model-based performance often translates well and is influenced mainly by heterogeneity and locality.

Contribution

It provides the first systematic evaluation of external validity for average-case graph algorithm analyses on real-world networks.

Findings

Model-based algorithm performance correlates well with real-world networks.

Heterogeneity and locality are key factors affecting algorithm performance.

Performance differences are primarily driven by these network properties.

Abstract

The number one criticism of average-case analysis is that we do not actually know the probability distribution of real-world inputs. Thus, analyzing an algorithm on some random model has no implications for practical performance. At its core, this criticism doubts the existence of external validity, i.e., it assumes that algorithmic behavior on the somewhat simple and clean models does not translate beyond the models to practical performance real-world input. With this paper, we provide a first step towards studying the question of external validity systematically. To this end, we evaluate the performance of six graph algorithms on a collection of 2740 sparse real-world networks depending on two properties; the heterogeneity (variance in the degree distribution) and locality (tendency of edges to connect vertices that are already close). We compare this with the performance on generated networks with varying locality and heterogeneity. We find that the performance in the idealized setting of network models translates surprisingly well to real-world networks. Moreover, heterogeneity and locality appear to be the core properties impacting the performance of many graph algorithms.