# DFOGraph: An I/O- and Communication-Efficient System for Distributed Fully-out-of-Core Graph Processing

arXiv: https://arxiv.org/abs/2101.06911 · HTML: https://mlsanity.com/paper/2101.06911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs](https://mlsanity.com/paper/2503.06208/) (2025)

## Abstract (arXiv)

With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While existing distributed out-of-core solutions have made it possible, they suffer from limited performance due to excessive I/O and communication costs. We present DFOGraph, a distributed fully-out-of-core graph processing system that applies and assembles multiple techniques to enable I/O- and communication-efficient processing. DFOGraph builds upon two-level column-oriented partition with adaptive compressed representations to allow fine-grained selective computation and communication, and it only issues necessary disk and network requests. Our evaluation shows DFOGraph achieves performance comparable to GridGraph and FlashGraph (>2.52x and 1.06x) on a single machine and outperforms Chaos and HybridGraph significantly (>12.94x and >10.82x) when scaling out.