# Study on Resource Efficiency of Distributed Graph Processing

## TL;DR

This paper reviews distributed graph processing systems, analyzes their strengths and weaknesses, and quantitatively compares two systems based on community detection performance to evaluate resource efficiency.

## Contribution

It provides a comprehensive classification of distributed graph processing systems and offers a detailed quantitative comparison of two systems for community detection.

## Key findings

- Identified key strengths and weaknesses of various systems.
- Quantitative analysis of community detection performance.
- Insights into resource efficiency of selected systems.

## Abstract

Graphs may be used to represent many different problem domains -- a concrete example is that of detecting communities in social networks, which are represented as graphs. With big data and more sophisticated applications becoming widespread in recent years, graph processing has seen an emergence of requirements pertaining data volume and volatility. This multidisciplinary study presents a review of relevant distributed graph processing systems. Herein they are presented in groups defined by common traits (distributed processing paradigm, type of graph operations, among others), with an overview of each system's strengths and weaknesses. The set of systems is then narrowed down to a set of two, upon which quantitative analysis was performed. For this quantitative comparison of systems, focus was cast on evaluating the performance of algorithms for the problem of detecting communities. To help further understand the evaluations performed, a background is provided on graph clustering.