# GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs

arXiv: https://arxiv.org/abs/2401.03114 · HTML: https://mlsanity.com/paper/2401.03114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 63 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 28 of 28)

- [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)
- [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 Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [A Note on Over-Smoothing for Graph Neural Networks](https://mlsanity.com/paper/2006.13318/) (2020)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [Reducing Communication in Graph Neural Network Training](https://mlsanity.com/paper/2005.03300/) (2020)
- [BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing](https://mlsanity.com/paper/2112.08541/) (2021)
- [PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication](https://mlsanity.com/paper/2203.10428/) (2022)
- [Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs](https://mlsanity.com/paper/2112.15345/) (2021)
- [Compressing Graphs and Indexes with Recursive Graph Bisection](https://mlsanity.com/paper/1602.08820/) (2016)
- [Distributed Edge Partitioning for Trillion-edge Graphs](https://mlsanity.com/paper/1908.05855/) (2019)
- [Partitioning Trillion-edge Graphs in Minutes](https://mlsanity.com/paper/1610.07220/) (2016)
- [Clustering-based Partitioning for Large Web Graphs](https://mlsanity.com/paper/2201.00472/) (2022)

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

- [DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System](https://mlsanity.com/paper/2503.01281/) (2025)

## Abstract (arXiv)

As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult to deploy GNNs on industrial scale graphs, due to their huge data size and complex topological structures. In this paper, we propose GLISP, a sampling based GNN learning system for industrial scale graphs. By exploiting the inherent structural properties of graphs, such as power law distribution and data locality, GLISP addresses the scalability and performance issues that arise at different stages of the graph learning process. GLISP consists of three core components: graph partitioner, graph sampling service and graph inference engine. The graph partitioner adopts the proposed vertex-cut graph partitioning algorithm AdaDNE to produce balanced partitioning for power law graphs, which is essential for sampling based GNN systems. The graph sampling service employs a load balancing design that allows the one hop sampling request of high degree vertices to be handled by multiple servers. In conjunction with the memory efficient data structure, the efficiency and scalability are effectively improved. The graph inference engine splits the K-layer GNN into K slices and caches the vertex embeddings produced by each slice in the data locality aware hybrid caching system for reuse, thus completely eliminating redundant computation caused by the data dependency of graph. Extensive experiments show that GLISP achieves up to 6.53× and 70.77× speedups over existing GNN systems for training and inference tasks, respectively, and can scale to the graph with over 10 billion vertices and 40 billion edges with limited resources.