# Fast and Robust Distributed Subgraph Enumeration

## TL;DR

RADS is a novel asynchronous distributed system for subgraph enumeration that reduces memory and communication costs, improves robustness, and outperforms existing methods on large graphs.

## Contribution

Introduces RADS, a robust asynchronous distributed framework with a region-grouped multi-round approach that minimizes data shuffling and enhances performance and scalability.

## Key findings

- RADS outperforms state-of-the-art approaches in experiments.
- Reduces network communication and memory usage.
- Improves robustness and load balancing in distributed subgraph enumeration.

## Abstract

We study the classic subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them impractical for very large graphs. Most of them follow a synchronous model where the performance is often bottlenecked by the machine with the worst performance. Motivated by this, in this paper, we propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system. RADS first identifies results that can be found using single-machine algorithms. This strategy not only improves the overall performance but also reduces network communication and memory cost. Moreover, RADS employs a novel region-grouped multi-round expand verify & filter framework which does not need to shuffle and exchange the intermediate results, nor does it need to replicate a large part of the data graph in each machine. This feature not only reduces network communication cost and memory usage, but also allows us to adopt simple strategies for memory control and load balancing, making it more robust. Several heuristics are also used in RADS to further improve the performance. Our experiments verified the superiority of RADS to state-of-the-art subgraph enumeration approaches.