Awesome-Large-Scale-Graph-Learning

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Awesome-Large-Scale-Graph-Learning

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A curated list of papers on large-scale graph learning.

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# Awesome-Large-Scale-Graph-Learning
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This repository contains a curated list of papers on **large-scale graph learning**, which are sorted by their published years.

*Continuously updating!*

-----
### Year 2024
(WWW 2024) **Macro Graph Neural Networks for Online Billion-Scale Recommender Systems** [[Paper]](https://arxiv.org/pdf/2401.14939.pdf) [[Code]](https://github.com/YuanchenBei/MacGNN)

(WWW 2024) **Linear-Time Graph Neural Networks for Scalable Recommendations** [[Paper]](https://arxiv.org/pdf/2402.13973.pdf) [[Code]](https://github.com/QwQ2000/TheWebConf24-LTGNN-PyTorch)

(ICLR 2024) **VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs** [[Paper]](https://openreview.net/pdf?id=h6Tz85BqRI) [[Code]](https://github.com/YangLing0818/VQGraph)

(ICLR 2024) **LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100 × Faster Inference** [[Paper]](https://arxiv.org/pdf/2402.04296.pdf)

(Arxiv 2024) **Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network** [[Paper]](https://arxiv.org/pdf/2403.13268.pdf)

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### Year 2023
(ICLR 2023) **Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency** [[Paper]](https://openreview.net/pdf?id=Cs3r5KLdoj) [[Code]](https://github.com/meettyj/NOSMOG)

(ICLR 2023) **MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization** [[Paper]](https://openreview.net/pdf?id=P8YIphWNEGO) [[Code]](https://github.com/snap-research/MLPInit-for-GNNs)

(NIPS 2023) **LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings** [[Paper]](https://openreview.net/pdf?id=7zkFc9TGKz) [[Code]](https://github.com/gdmnl/LD2)

(NIPS 2023) **Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data** [[Paper]](https://openreview.net/pdf?id=XkcufOcgUc) [[Code]](https://github.com/Amanda-Zheng/SFGC)

(ICML 2023) **LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation** [[Paper]](https://openreview.net/pdf?id=P98vAWoj5W) [[Code]](https://github.com/RXPHD/Lazy_GNN)

(ICML 2023) **Linkless Link Prediction via Relational Distillation** [[Paper]](https://proceedings.mlr.press/v202/guo23f/guo23f.pdf) [[Code]](https://github.com/snap-research/linkless-link-prediction/)

(ICML 2023) **GOAT: A Global Transformer on Large-scale Graphs** [[Paper]](https://proceedings.mlr.press/v202/kong23a/kong23a.pdf) [[Code]](https://github.com/devnkong/GOAT)

(AAAI 2023) **Scalable Spatiotemporal Graph Neural Networks** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/download/25880/25652) [[Code]](https://github.com/Graph-Machine-Learning-Group/sgp)

(CVPR 2023) **From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm** [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_From_Node_Interaction_To_Hop_Interaction_New_Effective_and_Scalable_CVPR_2023_paper.pdf) [[Code]](https://github.com/JC-202/HopGNN)

(VLDB 2023) **Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach** [[Paper]](https://www.vldb.org/pvldb/vol17/p175-xu.pdf) [[Code]](https://github.com/iBoom2333/AnchorGNN)

(ICDM 2023) **Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation** [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415671)

(RecSys 2023) **LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation** [[Paper]](https://arxiv.org/pdf/2310.19394.pdf)

(SDM 2023) **Adaptive Label Smoothing To Regularize Large-Scale Graph Training** [[Paper]](https://epubs.siam.org/doi/pdf/10.1137/1.9781611977653.ch7)

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### Year 2022
(ICLR 2022) **Large-Scale Representation Learning on Graphs via Bootstrapping** [[Paper]](https://openreview.net/pdf?id=0UXT6PpRpW) [[Code]](https://github.com/nerdslab/bgrl)

(ICLR 2022) **Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation** [[Paper]](https://openreview.net/pdf?id=4p6_5HBWPCw) [[Code]](https://github.com/snap-research/graphless-neural-networks)

(NIPS 2022) **Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2022/file/46027e3de0db3617a911f1a647def3bf-Paper-Conference.pdf) [[Code]](https://github.com/zyzisastudyreallyhardguy/Graph-Group-Discrimination)

(NIPS 2022) **A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2022/file/23ee05bf1f4ade71c0f8f5ca722df601-Paper-Datasets_and_Benchmarks.pdf) [[Code]](https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking)

(ICML 2022) **GraphFM: Improving Large-Scale GNN Training via Feature Momentum** [[Paper]](https://proceedings.mlr.press/v162/yu22g/yu22g.pdf)

(ICML 2022) **Large-Scale Graph Neural Architecture Search** [[Paper]](https://proceedings.mlr.press/v162/guan22d/guan22d.pdf) [[Code]](https://github.com/THUMNLab/GAUSS)

(KDD 2022) **Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph** [[Paper]](https://arxiv.org/pdf/2208.05648.pdf) [[Paper]](https://arxiv.org/pdf/2208.05648.pdf)

(KDD 2022) **Graph Attention Multi-Layer Perceptron** [[Paper]](https://arxiv.org/pdf/2206.04355.pdf) [[Code]](https://github.com/PKU-DAIR/GAMLP)

(SIGIR 2022) **An MLP-based Algorithm for Efficient Contrastive Graph Recommendations** [[Paper]](https://eprints.gla.ac.uk/269020/1/269020.pdf)

(AAAI 2022) **Beyond GNNs: An Efficient Architecture for Graph Problems** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/download/20548/20307)

(VLDB 2022) **ByteGNN: Efficient Graph Neural Network Training at Large Scale** [[Paper]](https://www.vldb.org/pvldb/https://www.vldb.org/pvldb/vol15/p1228-zheng.pdf)

(CIKM 2022) **AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training** [[Paper]](http://dl-acm-org-s.webvpn.zju.edu.cn:8001/doi/pdf/10.1145/3511808.3557228) [[Code]](https://github.com/YL-wang/CIKM_AdaGCL/)

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### Year 2021
(ICLR 2021) **Combining Label Propagation and Simple Models out-performs Graph Neural Networks** [[Paper]](https://openreview.net/pdf?id=8E1-f3VhX1o) [[Code]](https://github.com/CUAI/CorrectAndSmooth)

(ICLR 2021) **AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models** [[Paper]](https://openreview.net/pdf?id=QkRbdiiEjM) [[Code]](https://github.com/datake/AdaGCN)

(ICLR 2021) **On Graph Neural Networks versus Graph-Augmented MLPs** [[Paper]](https://openreview.net/pdf?id=tiqI7w64JG2) [[Code]](https://github.com/leichen2018/GNN_vs_GAMLP)

(NIPS 2021) **VQ-GNN: A Universal Framework to Scale-up Graph Neural Networks using Vector Quantization** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2021/file/3569df159ec477451530c4455b2a9e86-Paper.pdf) [[Code]](https://github.com/devnkong/VQ-GNN)

(NIPS 2021) **Node Dependent Local Smoothing for Scalable Graph Learning** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2021/file/a9eb812238f753132652ae09963a05e9-Paper.pdf)

(NIPS 2021) **A Large-Scale Database for Graph Representation Learning** [[Paper]](https://openreview.net/pdf?id=1xDTDk3XPW) [[Code]](https://www.mal-net.org/)

(NIPS 2021) **Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2021/file/ae816a80e4c1c56caa2eb4e1819cbb2f-Paper.pdf) [[Code]](https://github.com/CUAI/Non-Homophily-Large-Scale)

(ICML 2021) **GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings** [[Paper]](https://proceedings.mlr.press/v139/fey21a/fey21a.pdf)

(KDD 2021) **Pre-training on Large-Scale Heterogeneous Graph** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467396)

(WWW 2021) **Hashing-Accelerated Graph Neural Networks for Link Prediction** [[Paper]](https://arxiv.org/pdf/2105.14280.pdf) [[Code]](https://github.com/williamweiwu/williamweiwu.github.io/tree/master/Graph_Network%20Embedding/HashGNN)

(IJCAI 2021) **Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks** [[Paper]](https://cs.nju.edu.cn/lwj/paper/IJCAI21_BNS.pdf)

(SC 2021) **DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks** [[Paper]](https://arxiv.org/pdf/2104.06700.pdf)

(Arxiv 2021) **Graph-MLP: Node Classification without Message Passing in Graph** [[Paper]](https://arxiv.org/pdf/2106.04051.pdf) [[Code]](https://github.com/yanghu819/Graph-MLP)

-----
### Year 2020
(ICLR 2020) **GraphSAINT: Graph Sampling Based Inductive Learning Method** [[Paper]](https://openreview.net/pdf?id=BJe8pkHFwS) [[Code]](https://github.com/GraphSAINT/GraphSAINT)

(NIPS 2020) **Scalable Graph Neural Networks via Bidirectional Propagation** [[Paper]](https://proceedings.neurips.cc/paper/2020/file/a7789ef88d599b8df86bbee632b2994d-Paper.pdf)

(NIPS 2020) **Self-Supervised Graph Transformer on Large-Scale Molecular Data** [[Paper]](https://proceedings.neurips.cc/paper/2020/file/94aef38441efa3380a3bed3faf1f9d5d-Paper.pdf) [[Code]](https://github.com/tencent-ailab/grover)

(KDD 2020) **Scaling Graph Neural Networks with Approximate PageRank** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394486.3403296) [[Code]](https://github.com/TUM-DAML/pprgo_pytorch)

(WWW 2020) **Learning to Hash with Graph Neural Networks for Recommender Systems** [[Paper]](https://arxiv.org/pdf/2003.01917.pdf)

(ICDE 2020) **Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications** [[Paper]](https://drive.google.com/file/d/1KfnuTkQdPb6HKaJQaF5nmEpav08Em8lj/view)

(TKDE 2020) **GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs** [[Paper]](https://arxiv.org/pdf/2011.01393.pdf)

(TKDD 2020) **Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling** [[Paper]](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6890&context=sis_research)

(Arxiv 2020) **SIGN: Scalable Inception Graph Neural Networks** [[Paper]](https://arxiv.org/pdf/2004.11198.pdf)

-----
### Before & Year 2019

(ICLR 2019) **Large Scale Graph Learning from Smooth Signals** [[Paper]](https://arxiv.org/pdf/1710.05654.pdf) [[Code]](https://epfl-lts2.github.io/gspbox-html/doc/demos/gsp_demo_learn_graph_large.html)

(NIPS 2019) **Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks** [[Paper]](https://proceedings.neurips.cc/paper/2019/file/91ba4a4478a66bee9812b0804b6f9d1b-Paper.pdf) [[Code]](https://github.com/acbull/LADIES)

(ICML 2019) **Simplifying Graph Convolutional Networks** [[Paper]](https://proceedings.mlr.press/v97/wu19e/wu19e.pdf) [[Code]](https://github.com/Tiiiger/SGC)

(KDD 2019) **Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330925) [[Code]](https://github.com/benedekrozemberczki/ClusterGCN)

(Arxiv 2019) **Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training** [[Paper]](https://arxiv.org/pdf/2104.09376.pdf) [[Code]](https://github.com/skepsun/SAGN_with_SLE)

(ICLR 2018) **FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling** [[Paper]](https://openreview.net/pdf?id=rytstxWAW) [[Code]](https://github.com/matenure/FastGCN)

(KDD 2018) **Large-Scale Learnable Graph Convolutional Networks** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219819.3219947) [[Code]](https://github.com/divelab/lgcn/)

(KDD 2018) **Graph Convolutional Neural Networks for Web-Scale Recommender Systems** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3219819.3219890)

(ICML 2018) **Improved Large-Scale Graph Learning through Ridge Spectral Sparsification** [[Paper]](https://proceedings.mlr.press/v80/calandriello18a/calandriello18a.pdf)

(NIPS 2017) **Inductive Representation Learning on Large Graphs** [[Paper]](https://proceedings.neurips.cc/paper/2017/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf) [[Code]](https://github.com/williamleif/GraphSAGE)

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