# A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs

## TL;DR

This paper introduces a novel distributed, partition-centric algorithm for efficiently finding Euler circuits in large graphs across distributed systems, addressing scalability and memory challenges.

## Contribution

It presents a new parallel algorithm for Euler circuit detection that works on large graphs in distributed environments, improving scalability over existing methods.

## Key findings

- Successfully implemented on Apache Spark for large graphs
- Identified and addressed memory bottlenecks in the algorithm
- Demonstrated improved scalability and efficiency

## Abstract

Finding the Eulerian circuit in graphs is a classic problem, but inadequately explored for parallel computation. With such cycles finding use in neuroscience and Internet of Things for large graphs, designing a distributed algorithm for finding the Euler circuit is important. Existing parallel algorithms are impractical for commodity clusters and Clouds. We propose a novel partition-centric algorithm to find the Euler circuit, over large graphs partitioned across distributed machines and executed iteratively using a Bulk Synchronous Parallel (BSP) model. The algorithm finds partial paths and cycles within each partition, and refines these into longer paths by recursively merging the partitions. We describe the algorithm, analyze its complexity, validate it on Apache Spark for large graphs, and offer experimental results. We also identify memory bottlenecks in the algorithm and propose an enhanced design to address it.