Sampling methods for efficient training of graph convolutional networks: A survey
arXiv:2103.05872
Abstract
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.
arXiv commentAccepted by IEEE/CAA Journal of Automatica Sinica
References in corpus (24)
- Distributed Representations of Words and Phrases and their Compositionality
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Inductive Representation Learning on Large Graphs
- Neural Message Passing for Quantum Chemistry
- Spectral Networks and Locally Connected Networks on Graphs
- Deep Convolutional Networks on Graph-Structured Data
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- Simplifying Graph Convolutional Networks
- Representation Learning on Graphs with Jumping Knowledge Networks
- DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
- Predicting multicellular function through multi-layer tissue networks
- Towards Deeper Graph Neural Networks
- Gated Graph Sequence Neural Networks
- Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
- Adaptive Sampling Towards Fast Graph Representation Learning
- Graph-Bert: Only Attention is Needed for Learning Graph Representations
- Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
- Accurate, Efficient and Scalable Graph Embedding
- Explainability Techniques for Graph Convolutional Networks
- Bandit Samplers for Training Graph Neural Networks
- Bayesian Graph Neural Networks with Adaptive Connection Sampling
- Deep Graph Neural Networks with Shallow Subgraph Samplers
- Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network