# Out-of-Core Edge Partitioning at Linear Run-Time

arXiv: https://arxiv.org/abs/2203.12721 · HTML: https://mlsanity.com/paper/2203.12721/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training](https://mlsanity.com/paper/2308.15602/) (2023)
- [Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study](https://mlsanity.com/paper/2409.11129/) (2024)
- [SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks](https://mlsanity.com/paper/2404.02300/) (2024)
- [EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training](https://mlsanity.com/paper/2604.01000/) (2026)

## Abstract (arXiv)

Graph edge partitioning is an important preprocessing step to optimize distributed computing jobs on graph-structured data. The edge set of a given graph is split into k equally-sized partitions, such that the replication of vertices across partitions is minimized. Out-of-core edge partitioning algorithms are able to tackle the problem with low memory overhead. Exsisting out-of-core algorithms mainly work in a streaming manner and can be grouped into two types. While stateless streaming edge partitioning is fast and yields low partitioning quality, stateful streaming edge partitioning yields better quality, but is expensive, as it requires a scoring function to be evaluated for every edge on every partition, leading to a time complexity of O(|E|*k). In this paper, we propose 2PS-L, a novel out-of-core edge partitioning algorithm that builds upon the stateful streaming model, but achieves linear run-time (i.e., O(|E|)). 2PS-L consists of two phases. In the first phase, vertices are separated into clusters by a lightweight streaming clustering algorithm. In the second phase, the graph is re-streamed and vertex clustering from the first phase is exploited to reduce the search space of graph partitioning to only two target partitions for every edge. Our evaluations show that 2PS-L can achieve better partitioning quality than existing stateful streaming edge partitioners while having a much lower run-time. As a consequence, the total run-time of partitioning and subsequent distributed graph processing can be significantly reduced.