# A Survey and Experimental Analysis of Distributed Subgraph Matching

## TL;DR

This paper systematically compares distributed subgraph matching algorithms by implementing four strategies and three optimizations, providing insights into their performance and practical guidance for various matching scenarios.

## Contribution

It identifies key strategies and optimizations, implements them in a unified system, and conducts extensive experiments to analyze their effectiveness.

## Key findings

- Performance varies significantly across strategies and optimizations.
- The study offers practical guidelines for choosing algorithms based on matching types and settings.
- Implementation of all representation algorithms enables comprehensive comparison.

## Abstract

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pros and cons of each algorithm mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art works. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representation algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.