Awesome-Scalable-GNNs.md

Awsome-Scalable-GNNs

Contents

• Scalable-GNNs
  • 1.Linear Model
  • 2.Sampling

To scale GNNs to extremely large graphs, existing works can be classified into the following two types.

1.Linear Model

Source: Node Dependent Local Smoothing for Scalable Graph Learning

1. Simplifying Graph Convolutional Networks [ICML 2019] [paper] [code]
2. Scalable Graph Neural Networks via Bidirectional Propagation [NeurIPS 2020] [paper] [code]
3. SIGN: Scalable Inception Graph Neural Networks [ICML 2020] [paper] [code]
4. Simple Spectral Graph Convolution [ICLR 2021] [paper] [code]
5. Node Dependent Local Smoothing for Scalable Graph Learning [NeurIPS 2021] [paper] [code]
6. Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training [Arxiv 2021] [paper] [code]
7. Graph Attention Multi-Layer Perceptron [Arxiv 2021] [paper] [code]
8. NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning [OpenReview 2022] [paper] [code]

2.Sampling

Source: Inductive Representation Learning on Large Graphs

Node-wise sampling

1. Inductive Representation Learning on Large Graphs [NIPS 2017] [paper] [code]
2. Scaling Graph Neural Networks with Approximate PageRank [KDD 2020] [paper] [code]
3. Stochastic Training of Graph Convolutional Networks with Variance Reduction [ICML 2018] [paper] [code]
4. GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings [ICML 2021] [paper] [code]
5. Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018] [paper]

Layer-wise sampling

1. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling [ICLR 2018] [paper][code]
2. Accelerating Large Scale Real-Time GNN Inference using Channel Pruning [Arxiv 2021] [paper] [code]
3. Adaptive Sampling Towards Fast Graph Representation Learning [NeurIPS 2018] [paper] [code_pytorch] [code_tentsor_flow]

Graph-wise sampling

1. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks [KDD 2019] [paper] [code]
2. GraphSAINT: Graph Sampling Based Inductive Learning Method [ICLR 2020] [paper] [code]
3. Large-Scale Learnable Graph Convolutional Networks [KDD 2018] [paper] [code]