GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 63 references that are in the map. First posted on arXiv 6 Jan 2024.
Builds on
28 of 63 references.
References · 28
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
- 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
- 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
- 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
- 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
- 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
16 more
- 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
- 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
- 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
- 2020A Note on Over-Smoothing for Graph Neural Networkscited by 245
- 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 152
- 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
- 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
- 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
- 2020Reducing Communication in Graph Neural Network Trainingcited by 50
- 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
- 2022PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communicationcited by 45
- 2021Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphscited by 33
- 2016Compressing Graphs and Indexes with Recursive Graph Bisectioncited by 8
- 2019Distributed Edge Partitioning for Trillion-edge Graphscited by 7
- 2016Partitioning Trillion-edge Graphs in Minutescited by 3
- 2022Clustering-based Partitioning for Large Web Graphscited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.