Node2Grids: A Cost-Efficient Uncoupled Training Framework for Large-Scale Graph Learning
Journal
International Conference on Information and Knowledge Management, Proceedings
Pages
2281-2290
Date Issued
2021
Author(s)
Abstract
Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) are inherently coupled training frameworks repetitively conducting the recursive neighborhood aggregation, which leads to high computational and memory overheads when processing large-scale graphs. To tackle these issues, we present Node2Grids, a cost-efficient uncoupled training framework that leverages the independent mapped data for obtaining the embedding. Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the uncoupled models as Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resources while achieving comparable results with the leading GCN-based models. Specifically, in order to support a general and convenient mapping approach, Node2Grids selects the most influential neighborhood with central node fusion information to construct the grid-like data. To further improve the downstream tasks' efficiency, a simple CNN-based neural network is employed to capture the significant information from the mapped grid-like data. Moreover, the grid-level attention mechanism is implemented, which enables implicitly specifying the different weights for the extracted grids of CNN. In addition to the typical transductive and inductive learning tasks, we also verify our framework on million-scale graphs to demonstrate the superiority of cost performance against the state-of-the-art GCN-based approaches. The codes are available on the GitHub link. ? 2021 ACM.
Subjects
graph convolutional network
large-scale graph learning
uncoupled training
Convolution
Graph neural networks
Learning systems
Mapping
Convolutional networks
Convolutional neural network
Cost-efficient
Graph convolutional network
Grid-like
Large-scale graph learning
Large-scales
Network-based modeling
Training framework
Uncoupled training
Convolutional neural networks
Type
conference paper