# InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs

arXiv: https://arxiv.org/abs/2307.00228 · HTML: https://mlsanity.com/paper/2307.00228/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 59 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 25 of 25)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [Adaptive Sampling Towards Fast Graph Representation Learning](https://mlsanity.com/paper/1809.05343/) (2018)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation](https://mlsanity.com/paper/2110.08727/) (2021)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [Predicting multicellular function through multi-layer tissue networks](https://mlsanity.com/paper/1707.04638/) (2017)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [Accelerating Large Scale Real-Time GNN Inference using Channel Pruning](https://mlsanity.com/paper/2105.04528/) (2021)
- [Bandit Samplers for Training Graph Neural Networks](https://mlsanity.com/paper/2006.05806/) (2020)
- [Graph-based Neural Sentence Ordering](https://mlsanity.com/paper/1912.07225/) (2019)
- [Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation](https://mlsanity.com/paper/2111.02100/) (2021)
- [Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing](https://mlsanity.com/paper/2003.01515/) (2020)
- [Private Information, Credit Risk and Graph Structure in P2P Lending Networks](https://mlsanity.com/paper/1802.10000/) (2018)
- [GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching](https://mlsanity.com/paper/2105.10554/) (2021)
- [DSSLP: A Distributed Framework for Semi-supervised Link Prediction](https://mlsanity.com/paper/2002.12056/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training](https://mlsanity.com/paper/2407.03953/) (2024)
- [Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving](https://mlsanity.com/paper/2504.07494/) (2025)
- [Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs](https://mlsanity.com/paper/2607.17374/) (2026)
- [PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration](https://mlsanity.com/paper/2608.04255/) (2026)

## Abstract (arXiv)

GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address the above challenges, we propose a scalable system named InferTurbo to boost the GNN inference tasks in industrial scenarios. Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference. The computation of GNNs is expressed in an iteration manner, in which a vertex would gather messages via in-edges and update its state information by forwarding an associated layer of GNNs with those messages and then send the updated information to other vertexes via out-edges. Following the schema, the proposed InferTurbo can be built with alternative backends (e.g., batch processing system or graph computing system). Moreover, InferTurbo introduces several strategies like shadow-nodes and partial-gather to handle nodes with large degrees for better load balancing. With InferTurbo, GNN inference can be hierarchically conducted over the full graph without sampling and redundant computation. Experimental results demonstrate that our system is robust and efficient for inference tasks over graphs containing some hub nodes with many adjacent edges. Meanwhile, the system gains a remarkable performance compared with the traditional inference pipeline, and it can finish a GNN inference task over a graph with tens of billions of nodes and hundreds of billions of edges within 2 hours.