This repository contains a curated list of papers on large-scale graph learning, which are sorted by their published years.
Continuously updating!
(WWW 2024) Macro Graph Neural Networks for Online Billion-Scale Recommender Systems [Paper] [Code]
(WWW 2024) Linear-Time Graph Neural Networks for Scalable Recommendations [Paper] [Code]
(ICLR 2024) VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs [Paper] [Code]
(ICLR 2024) LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100 × Faster Inference [Paper]
(Arxiv 2024) Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network [Paper]
(ICLR 2023) Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency [Paper] [Code]
(ICLR 2023) MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization [Paper] [Code]
(NIPS 2023) LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings [Paper] [Code]
(NIPS 2023) Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data [Paper] [Code]
(ICML 2023) LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation [Paper] [Code]
(ICML 2023) Linkless Link Prediction via Relational Distillation [Paper] [Code]
(ICML 2023) GOAT: A Global Transformer on Large-scale Graphs [Paper] [Code]
(AAAI 2023) Scalable Spatiotemporal Graph Neural Networks [Paper] [Code]
(CVPR 2023) From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm [Paper] [Code]
(VLDB 2023) Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach [Paper] [Code]
(ICDM 2023) Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation [Paper]
(RecSys 2023) LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation [Paper]
(SDM 2023) Adaptive Label Smoothing To Regularize Large-Scale Graph Training [Paper]
(ICLR 2022) Large-Scale Representation Learning on Graphs via Bootstrapping [Paper] [Code]
(ICLR 2022) Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation [Paper] [Code]
(NIPS 2022) Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination [Paper] [Code]
(NIPS 2022) A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [Paper] [Code]
(ICML 2022) GraphFM: Improving Large-Scale GNN Training via Feature Momentum [Paper]
(ICML 2022) Large-Scale Graph Neural Architecture Search [Paper] [Code]
(KDD 2022) Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph [Paper] [Paper]
(KDD 2022) Graph Attention Multi-Layer Perceptron [Paper] [Code]
(SIGIR 2022) An MLP-based Algorithm for Efficient Contrastive Graph Recommendations [Paper]
(AAAI 2022) Beyond GNNs: An Efficient Architecture for Graph Problems [Paper]
(VLDB 2022) ByteGNN: Efficient Graph Neural Network Training at Large Scale [Paper]
(CIKM 2022) AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training [Paper] [Code]
(ICLR 2021) Combining Label Propagation and Simple Models out-performs Graph Neural Networks [Paper] [Code]
(ICLR 2021) AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models [Paper] [Code]
(ICLR 2021) On Graph Neural Networks versus Graph-Augmented MLPs [Paper] [Code]
(NIPS 2021) VQ-GNN: A Universal Framework to Scale-up Graph Neural Networks using Vector Quantization [Paper] [Code]
(NIPS 2021) Node Dependent Local Smoothing for Scalable Graph Learning [Paper]
(NIPS 2021) A Large-Scale Database for Graph Representation Learning [Paper] [Code]
(NIPS 2021) Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods [Paper] [Code]
(ICML 2021) GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings [Paper]
(KDD 2021) Pre-training on Large-Scale Heterogeneous Graph [Paper]
(WWW 2021) Hashing-Accelerated Graph Neural Networks for Link Prediction [Paper] [Code]
(IJCAI 2021) Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks [Paper]
(SC 2021) DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks [Paper]
(Arxiv 2021) Graph-MLP: Node Classification without Message Passing in Graph [Paper] [Code]
(ICLR 2020) GraphSAINT: Graph Sampling Based Inductive Learning Method [Paper] [Code]
(NIPS 2020) Scalable Graph Neural Networks via Bidirectional Propagation [Paper]
(NIPS 2020) Self-Supervised Graph Transformer on Large-Scale Molecular Data [Paper] [Code]
(KDD 2020) Scaling Graph Neural Networks with Approximate PageRank [Paper] [Code]
(WWW 2020) Learning to Hash with Graph Neural Networks for Recommender Systems [Paper]
(ICDE 2020) Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications [Paper]
(TKDE 2020) GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs [Paper]
(TKDD 2020) Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling [Paper]
(Arxiv 2020) SIGN: Scalable Inception Graph Neural Networks [Paper]
(ICLR 2019) Large Scale Graph Learning from Smooth Signals [Paper] [Code]
(NIPS 2019) Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks [Paper] [Code]
(ICML 2019) Simplifying Graph Convolutional Networks [Paper] [Code]
(KDD 2019) Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks [Paper] [Code]
(Arxiv 2019) Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training [Paper] [Code]
(ICLR 2018) FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling [Paper] [Code]
(KDD 2018) Large-Scale Learnable Graph Convolutional Networks [Paper] [Code]
(KDD 2018) Graph Convolutional Neural Networks for Web-Scale Recommender Systems [Paper]
(ICML 2018) Improved Large-Scale Graph Learning through Ridge Spectral Sparsification [Paper]
(NIPS 2017) Inductive Representation Learning on Large Graphs [Paper] [Code]