# GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs

arXiv: https://arxiv.org/abs/2503.06212 · HTML: https://mlsanity.com/paper/2503.06212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket](https://mlsanity.com/paper/2105.08664/) (2021)
- [GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy](https://mlsanity.com/paper/2104.10569/) (2021)

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

- [GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks](https://mlsanity.com/paper/2609.08970/) (2026)

## Abstract (arXiv)

Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges. To enable efficient training on such large graphs, mini-batch subgraph sampling is commonly used, which allows training without loading the entire graph into memory. However, existing solutions face significant trade-offs: online subgraph generation, as seen in frameworks like DGL and PyG, is limited to a single machine, resulting in severe performance bottlenecks, while offline precomputed subgraphs, as in GraphGen, improve sampling efficiency but introduce large storage overhead and high I/O costs during training. To address these challenges, we propose GraphGen+, an integrated framework that synchronizes distributed subgraph generation with in-memory graph learning, eliminating the need for external storage while significantly improving efficiency. GraphGen+ achieves a 27× speedup in subgraph generation compared to conventional SQL-like methods and a 1.3× speedup over GraphGen, supporting training on 1 million nodes per iteration and removing the overhead associated with precomputed subgraphs, making it a scalable and practical solution for industry-scale graph learning.