{"id":40988,"url":"https://github.com/THUMNLab/awesome-auto-graph-learning","name":"awesome-auto-graph-learning","description":"A paper collection about automated graph learning","projects_count":123,"last_synced_at":"2026-07-31T13:00:37.356Z","repository":{"id":43205399,"uuid":"390589660","full_name":"THUMNLab/awesome-auto-graph-learning","owner":"THUMNLab","description":"A paper collection about automated graph learning","archived":false,"fork":false,"pushed_at":"2024-06-08T02:55:33.000Z","size":115,"stargazers_count":99,"open_issues_count":0,"forks_count":8,"subscribers_count":10,"default_branch":"main","last_synced_at":"2026-07-12T11:03:19.540Z","etag":null,"topics":["automl","deep-learning","graph","graph-neural-networks","hyper-parameter-optimization","machine-learning","neural-architecture-search"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/THUMNLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-07-29T03:33:34.000Z","updated_at":"2026-06-28T03:25:46.000Z","dependencies_parsed_at":"2024-01-16T15:44:27.652Z","dependency_job_id":"a9c7fc79-a992-4025-9415-8eb1fa0f47b8","html_url":"https://github.com/THUMNLab/awesome-auto-graph-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/THUMNLab/awesome-auto-graph-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2Fawesome-auto-graph-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2Fawesome-auto-graph-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2Fawesome-auto-graph-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2Fawesome-auto-graph-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/THUMNLab","download_url":"https://codeload.github.com/THUMNLab/awesome-auto-graph-learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2Fawesome-auto-graph-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36118274,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-31T02:00:06.731Z","response_time":112,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T21:18:02.051Z","updated_at":"2026-07-31T13:00:37.356Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Graph NAS","Miscellaneous","Survey","Graph HPO","Applications","Cite","Tool"],"sub_categories":["2020","2021","Self-supervised Learning","2023","2022","2019","Finance","Biology","Knowledge Graph Embedding","Others","Structure Learning","Explanation","2024"],"readme":"# awesome-auto-graph-learning\nThis is a paper collection about **automated graph learning**, i.e., fusing AutoML and graph learning. Two special focuses are **graph hyper-parameter optimization (HPO)** and **graph neural architecture search (NAS)**.\n\n**Please submit a pull request if you want to add new papers or have any suggestions!**\n\n\n## Survey\n* [IJCAI 2021] **Automated Machine Learning on Graphs: A Survey** [(Paper)](https://arxiv.org/abs/2103.00742)\n* [Extension] **Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions** [(Paper)](https://arxiv.org/abs/2201.01288)\n* (In Chinese)[计算机学报 2023] **图神经架构搜索综述** [(Paper)](http://cjc.ict.ac.cn/online/onlinepaper/zzw-202375101026.pdf)\n\n## Tool\n* [ICLR 2021 GTRL workshop] **AutoGL: A Library for Automated Graph Learning** [(Code)](https://github.com/THUMNLab/AutoGL) [(Homepage)](https://mn.cs.tsinghua.edu.cn/AutoGL) [(Paper)](https://openreview.net/pdf?id=0yHwpLeInDn) \n* **Lightweight Auto Graph Learning** [(Code)](https://github.com/THUMNLab/AutoGL-light) [(Paper)](https://arxiv.org/abs/2104.04987) \n\n## Graph NAS\n### 2024\n* [AAAI 2024] **Multimodal Graph Neural Architecture Search Under Distribution Shifts** [(Paper)](http://mn.cs.tsinghua.edu.cn/xinwang/PDF/papers/2024_Multimodal%20Graph%20Neural%20Architecture%20Search%20Under%20Distribution%20Shifts.pdf)\n* [AAAI 2024] **Data-Augmented Curriculum Graph Neural Architecture Search Under Distribution Shifts** [(Paper)](http://mn.cs.tsinghua.edu.cn/xinwang/PDF/papers/2024_Data-augmented%20Curriculum%20Graph%20Neural%20Architecture%20Search%20Under%20Distribution%20Shifts.pdf)\n### 2023\n* [NeurIPS 2023] **Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision** [(Paper)](https://openreview.net/forum?id=UAFa5ZhR85) [(Code)](https://github.com/wondergo2017/dsgas)\n* [NeurIPS 2023] **Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum** [(Paper)](https://openreview.net/forum?id=TOxpAwp0VE) [(Code)](https://github.com/THUMNLab/AutoGL-light)\n* [NeurIPS 2023] **GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection** [(Paper)](https://openreview.net/forum?id=5HisVXnx0n)\n* [CIKM 2023] **Node-dependent Semantic Search over Heterogeneous Graph Neural Networks** [(Paper)](http://www.shichuan.org/doc/154.pdf) [(Code)] https://github.com/BUPT-GAMMA/NDS\n* [ICML 2023] **Do Not Train It A Linear Neural Architecture Search of Graph Neural Networks** [(Paper)](https://arxiv.org/abs/2305.14065) \n* [ICLR 2023] **AutoGT Automated Graph Transformer Architecture Search** [(Paper)](https://openreview.net/forum?id=GcM7qfl5zY)\n* [WWW 2023] **Auto-HeG Automated Graph Neural Network on Heterophilic Graphs** [(Paper)](https://arxiv.org/abs/2302.12357)\n* [WWW 2023] **Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification** [(Paper)](https://arxiv.org/pdf/2302.08671.pdf)\n* [AAAI 2023] **Dynamic Heterogeneous Graph Attention Neural Architecture Search** [(Paper)](https://zw-zhang.github.io/files/2023_AAAI_DHGAS.pdf) [(Code)](https://github.com/wondergo2017/DHGAS)\n* [AAAI 2023] **Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks** [(Paper)](https://arxiv.org/abs/2211.14752) [(Code)](https://github.com/JHL-HUST/PMMM)\n* [ICDE 2023] **PSP Progressive Space Pruning for Efficient Graph Neural Architecture Search** [(Paper)](https://ieeexplore.ieee.org/document/9835246/)\n* [TKDE 2023] **Automated Graph Neural Network Search under Federated Learning Framework** [(Paper)](https://ieeexplore.ieee.org/document/10056291/)\n* [TKDE 2023] **HGNAS++ Efficient Architecture Search for Heterogeneous Graph Neural Networks** [(Paper)](https://ieeexplore.ieee.org/document/10040227)\n* [TOIS 2023] **Neural Architecture Search for GNN-based Graph Classification** [(Paper)](https://dl.acm.org/doi/10.1145/3584945)\n* [AI 2023] **AutoSTG+: An automatic framework to discover the optimal network for spatio-temporal graph prediction** [(Paper)](https://www.sciencedirect.com/science/article/pii/S0004370223000450)\n* [TETC 2023] **CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection** [(Paper)](https://ieeexplore.ieee.org/document/10112632)\n* [EngAppAI 2023] **Meta-GNAS Meta-reinforcement learning for graph neural architecture search** [(Paper)](https://www.sciencedirect.com/science/article/pii/S0952197623004840)\n* [KAIS 2023] **GM2NAS: multitask multiview graph neural architecture** [(Paper)](https://link.springer.com/article/10.1007/s10115-023-01886-7)\n* [ASOC 2023] **A surrogate evolutionary neural architecture search algorithm for graph neural networks search** [(Paper)](https://www.sciencedirect.com/science/article/abs/pii/S1568494623005033) [(Code)](https://github.com/chnyliu/CTFGNAS)\n* [arXiv 2023] **Efficient and Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search** [(Paper)](https://arxiv.org/abs/2308.15734) [(Code)](https://github.com/OnizukaLab/AutoGNN_mcts)\n### 2022\n* [TKDD 2022] **Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search** [(Paper)](https://arxiv.org/abs/2010.07474)\n* [ICDM 2022] **Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing** [(Paper)](https://ieeexplore.ieee.org/document/10027750/)\n* [NeurIPS 2022] **NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search** [(Paper)](https://arxiv.org/abs/2206.09166)[(Code)](https://github.com/THUMNLab/NAS-Bench-Graph)\n* [CIKM 2022] **GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search** [(Paper)](https://arxiv.org/pdf/2208.09027.pdf) [(Code)](https://github.com/fxsxjtu/GraTO)\n* [ICML 2022] **Large-Scale Graph Neural Architecture Search** [(Paper)](https://zw-zhang.github.io/files/2022_ICML_GAUSS.pdf) [(Code)](https://github.com/THUMNLab/GAUSS)\n* [ICML 2022] **Graph Neural Architecture Search Under Distribution Shifts** [(Paper)](https://zw-zhang.github.io/files/2022_ICML_GRACES.pdf)\n* [ICML 2022] **DFG-NAS: Deep and Flexible Graph Neural Architecture Search** [(Paper)](https://arxiv.org/abs/2206.08582) [(Code)](https://github.com/PKU-DAIR/DFG-NAS)\n* [KDD 2022] **Graph Neural Networks with Node-wise Architecture** [(Paper)](https://www.bolin-ding.com/papers/kdd22nwgnn.pdf)\n* [KDDDLG 2022] **Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search** [(Paper)](https://arxiv.org/abs/2207.06027) [(Code)](https://github.com/AutoML-Research/PAS-OGB)\n* [SIGIR 2022] **AutoGSR: Neural Architecture Search for Graph-based Session Recommendation** [(Paper)](https://dl.acm.org/doi/10.1145/3477495.3531940)\n* [TKDE 2022] **GraphNAS++: Distributed Architecture Search for Graph Neural Network** [(Paper)](https://ieeexplore.ieee.org/document/9782531)\n* [CVPR 2022] **Automatic Relation-aware Graph Network Proliferation** [(Paper)](https://arxiv.org/abs/2205.15678) [(Code)](https://github.com/phython96/ARGNP)\n* [WWW 2022] **PaSca a Graph Neural Architecture Search System under the Scalable Paradigm** [(Paper)](https://arxiv.org/abs/2203.00638) [(Code)](https://github.com/PKU-DAIR/SGL)\n* [WWW 2022] **Designing the Topology of Graph Neural Networks A Novel Feature Fusion Perspective** [(Paper)](https://arxiv.org/abs/2112.14531) [(Code)](https://github.com/AutoML-Research/F2GNN)\n* [ICDE 2022] **AutoHEnsGNN Winning Solution to AutoGraph Challenge for KDD Cup 2020** [(Paper)](https://arxiv.org/abs/2111.12952) [(Code)](https://github.com/aister2020/KDDCUP_2020_AutoGraph_1st_Place)\n* [TPDS 2022] **Auto-GNAS A Parallel Graph Neural Architecture Search Framework** [(Paper)](https://ieeexplore.ieee.org/document/9714826) \n* [WSDM 2022] **Profiling the Design Space for Graph Neural Networks based Collaborative Filtering** [(Paper)](http://www.shichuan.org/doc/125.pdf) [(Code)](https://github.com/BUPT-GAMMA/Design-Space-for-GNN-based-CF)\n* [ESWA 2022] **Efficient graph neural architecture search using Monte Carlo Tree search and prediction network** [(Paper)](https://www.sciencedirect.com/science/article/pii/S0957417422019340)\n* [Applied Intelligence] **Automatic search of architecture and hyperparameters of graph convolutional networks for node classification** [(Paper)](https://link.springer.com/article/10.1007/s10489-022-04096-w)\n* [arXiv 2022] **AutoKE: An automatic knowledge embedding framework for scientific machine learning** [(Paper)](https://arxiv.org/abs/2205.05390)\n* [arXiv 2022] **Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach** [(Paper)](https://arxiv.org/abs/2211.10990)\n\n### 2021\n* [NeurIPS 2021] **Graph Differentiable Architecture Search with Structure Learning** [(Paper)](https://openreview.net/forum?id=kSv_AMdehh3) [(Code)](https://github.com/THUMNLab/AutoGL)\n* [NeurIPS 2021] **AutoGEL: An Automated Graph Neural Network with Explicit Link Information** [(Paper)](https://openreview.net/forum?id=PftCCiHVQP) [(Code)](https://github.com/zwangeo/AutoGEL)\n* [ICDM 2021] **Heterogeneous Graph Neural Architecture Search** [(Paper)](https://ieeexplore.ieee.org/document/9679011)\n* [IJCNN 2021] **Automated Graph Representation Learning for Node Classification** [(Paper)](https://ieeexplore.ieee.org/document/9533811)\n* [PRICAI 2021] **ALGNN Auto-Designed Lightweight Graph Neural Network** [(Paper)](https://link.springer.com/chapter/10.1007/978-3-030-89188-6_37) \n* [CIKM 2021] **Pooling Architecture Search for Graph Classification** [(Paper)](https://arxiv.org/pdf/2108.10587.pdf) [(Code)](https://github.com/AutoML-Research/PAS)\n* [KDD 2021] **DiffMG Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks** [(Paper)](https://arxiv.org/abs/2010.03250) [(Code)](https://github.com/AutoML-4Paradigm/DiffMG)\n* [KDD 2021 DLG Workshop] **Learn Layer-wise Connections in Graph Neural Networks** [(Paper)](https://drive.google.com/file/d/11BxUT80T7FfjbM55YjpX-yvnoxRERCIN/view)\n* [ICML 2021] **AutoAttend Automated Attention Representation Search** [(Paper)](http://proceedings.mlr.press/v139/guan21a/guan21a.pdf)\n* [SIGIR 2021] **GraphPAS Parallel Architecture Search for Graph Neural Networks** [(Paper)](https://dl.acm.org/doi/abs/10.1145/3404835.3463007)\n* [CVPR 2021] **Rethinking Graph Neural Network Search from Message-passing** [(Paper)](https://arxiv.org/abs/2103.14282) [(Code)](https://github.com/phython96/GNAS-MP)\n* [GECCO 2021] **Fitness Landscape Analysis of Graph Neural Network Architecture Search Spaces** [(Paper)](https://dl.acm.org/doi/10.1145/3449639.3459318) [(Code)](https://github.com/mhnnunes/fla_nas_gnn)\n* [EuroSys 2021 EuroMLSys workshop] **Learned low precision graph neural networks** [(Paper)](https://arxiv.org/abs/2009.09232)\n* [WWW 2021]  **Autostg: Neural architecture search for predictions of spatio-temporal graphs** [(Paper)](http://panzheyi.cc/publication/pan2021autostg/paper.pdf) [(Code)](https://github.com/panzheyi/AutoSTG)\n* [ICDE 2021] **Search to aggregate neighborhood for graph neural network** [(Paper)](https://arxiv.org/abs/2104.06608) [(Code)](https://github.com/AutoML-4Paradigm/SANE)\n* [AAAI 2021] **One-shot graph neural architecture search with dynamic search space** [(Paper)](https://www.aaai.org/AAAI21Papers/AAAI-3441.LiY.pdf)\n* [arXiv] **Search For Deep Graph Neural Networks** [(Paper)](https://arxiv.org/pdf/2109.10047.pdf)\n* [arXiv] **G-CoS GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency** [(Paper)](https://arxiv.org/pdf/2109.08983.pdf)\n* [arXiv] **Edge-featured Graph Neural Architecture Search** [(Paper)](https://arxiv.org/pdf/2109.0135.pdf)\n* [arXiv] **FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search** [(Paper)](https://arxiv.org/abs/2104.04141)\n\n### 2020\n* [NeurIPS 2020] **Design space for graph neural networks** [(Paper)](https://arxiv.org/abs/2011.08843) [(Code)](https://github.com/snap-stanford/GraphGym)\n* [ICONIP 2020] **Autograph: Automated graph neural network** [(Paper)](https://arxiv.org/abs/2011.11288)\n* [BigData 2020] **Graph neural network architecture search for molecular property prediction** [(Paper)](https://arxiv.org/abs/2008.12187) [(Code)](https://github.com/deephyper/nas-gcn)\n* [CIKM 2020] **Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network** [(Paper)](https://arxiv.org/pdf/2102.10550) [(Code)](https://github.com/0oshowero0/GEMS)\n* [CIKM 2020 CSSA workshop] **Simplifying architecture search for graph neural network**[(Paper)](https://arxiv.org/abs/2008.11652) [(Code)](https://github.com/AutoML-4Paradigm/SNAG)\n* [BRACIS 2020] **Neural architecture search in graph neural networks** [(Paper)](https://arxiv.org/abs/2008.00077) [(Code)](https://github.com/mhnnunes/nas_gnn)\n* [IJCAI 2020] **Graph neural architecture search** [(Paper)](https://www.ijcai.org/proceedings/2020/195) [(Code)](https://github.com/GraphNAS/GraphNAS)\n* [CVPR 2020] **SGAS: Sequential Greedy Architecture Search** [(Paper)](https://arxiv.org/abs/1912.00195) [(Code)](https://github.com/lightaime/sgas)\n* [AAAI 2020] **Learning graph convolutional network for skeleton-based human action recognition by neural searching** [(Paper)](https://arxiv.org/abs/1911.04131) [(Code)](https://github.com/xiaoiker/GCN-NAS)\n* [OpenReview 2020] **Efficient graph neural architecture search** [(Paper)](https://openreview.net/forum?id=IjIzIOkK2D6)\n* [OpenReview 2020] **FGNAS: FPGA-Aware Graph Neural Architecture Search** [(Paper)](https://openreview.net/forum?id=cq4FHzAz9eA)\n* [arXiv 2020] **Evolutionary architecture search for graph neural networks** [(Paper)](https://arxiv.org/abs/2009.10199) [(Code)](https://github.com/IRES-FAU/Evolutionary-Architecture-Search-for-Graph-Neural-Networks) \n* [arXiv 2020] **Probabilistic dual network architecture search on graphs** [(Paper)](https://arxiv.org/abs/2003.09676)\n\n### 2019\n* [arXiv 2019] **Auto-gnn: Neural architecture search of graph neural networks** [(Paper)](https://arxiv.org/abs/1909.03184)\n\n## Graph HPO\n### 2023\n* [TKDE 2023] **Revisiting Embedding Based Graph Analyses Hyperparameters Matter** [(Paper)](https://ieeexplore.ieee.org/abstract/document/9994037)\n### 2022\n* [CIKM 2022] **Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty** [(Paper)](https://zxj32.github.io/data/CIKM_2022.pdf) \n* [KAIS 2022] **Autonomous graph mining algorithm search with best performance trade-off** [(Paper)](https://link.springer.com/article/10.1007/s10115-022-01683-8)\n* [ACL 2022] **KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning** [(Paper)](https://arxiv.org/pdf/2205.02460.pdf)\n* [arXiv 2022] **Start Small, Think Big On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings** [(Paper)](https://arxiv.org/abs/2207.04979)\n* [arXiv 2022] **Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality** [(Paper)](https://arxiv.org/abs/2207.00473)\n### 2021\n* [ICML 2021] **Explainable Automated Graph Representation Learning with Hyperparameter Importance** [(Paper)](http://proceedings.mlr.press/v139/wang21f/wang21f.pdf)\n* [SIGIR 2021] **Automated Graph Learning via Population Based Self-Tuning GCN** [(Paper)](https://arxiv.org/abs/2107.04713)\n* [PRICAI 2021] **Automatic Graph Learning with Evolutionary Algorithms: An Experimental Study** [(Paper)](https://link.springer.com/chapter/10.1007/978-3-030-89188-6_38) \n* [GECCO 2021] **Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction** [(Paper)](https://arxiv.org/pdf/2104.06046.pdf)\n* [P2PNA 2021] **ASFGNN Automated separated-federated graph neural network** [(Paper)](https://arxiv.org/abs/2011.03248)\n* [arXiv 2021] **A novel genetic algorithm with hierarchical evaluation strategy for hyperparameter optimisation of graph neural networks** [(Paper)](https://arxiv.org/abs/2101.09300)\n* [arXiv 2021] **Jitune: Just-in-time hyperparameter tuning for network embedding algorithms** [(Paper)](https://arxiv.org/abs/2101.06427)\n\n### 2020\n* [ICDM 2020] **Autonomous graph mining algorithm search with best speed/accuracy trade-off** [(Paper)](https://arxiv.org/abs/2011.14925) [(Code)](https://github.com/minjiyoon/ICDM20-AutoGM)\n\n### 2019\n* [KDD 2019] **AutoNE: Hyperparameter optimization for massive network embedding** [(Paper)](http://pengcui.thumedialab.com/papers/AutoNE.pdf) [(Code)](https://github.com/tadpole/AutoNE)\n\n## Applications\n### Finance\n* [CIKM 2022] **Explainable Graph-based Fraud Detection via Neural Meta-graph Search** [(Paper)](https://ponderly.github.io/pub/NGS_CIKM2022.pdf)\n### Biology\n* [JBHI 2024] **AutoDDI: Drug–drug Interaction Prediction with Automated Graph Neural Network** [(Paper)](https://ieeexplore.ieee.org/document/10380606)\n* [Bioinformatics 2023] **Cancer Drug Response Prediction With Surrogate Modeling-Based Graph Neural Architecture Search** [(Paper)](https://academic.oup.com/bioinformatics/article/39/8/btad478/7239861) [(Code)](https://github.com/BeObm/AutoCDRP)\n* [arXiv 2023] **Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search** [(Paper)](https://arxiv.org/abs/2307.10438) [(Code)](https://github.com/zavalab/ML/tree/master/AUTOGNNUQ)\n* [TCBB 2022] **Multi-view Graph Neural Architecture Search for Biomedical Entity and Relation Extraction** [(Paper)](https://ieeexplore.ieee.org/document/9881878)\n* [TCBB 2022] **AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction** [(Paper)](https://ieeexplore.ieee.org/document/9864145)\n* [AILSCI 2022] **AutoGGN: A gene graph network AutoML tool for multi-omics research** [(Paper)](https://www.biorxiv.org/content/10.1101/2021.04.30.442074v2)\n* [BIBM 2021] **Multi-label Metabolic Pathway Prediction with Auto Molecular Structure Representation Learning** [(Paper)](https://ieeexplore.ieee.org/document/9669309)\n### Knowledge Graph Embedding\n* [arXiv 2021] **AutoSF+: Towards Automatic Scoring Function Design for Knowledge Graph Embedding** [(Paper)](https://arxiv.org/abs/2107.00184)\n* [ICDE 2020] **AutoSF: Searching Scoring Functions for Knowledge Graph Embedding** [(Paper)](https://arxiv.org/abs/1904.11682) [(Code)](https://github.com/AutoML-4Paradigm/AutoSF)\n### Others\n* [arXiv 2024] **AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search** [(Paper)](https://arxiv.org/abs/2402.01313)\n* [CIKM 2023] **Retrieving GNN Architecture for Collaborative Filtering** [(Paper)](http://www.shichuan.org/doc/155.pdf)\n* [CASES 2023] **MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment** [(Paper)](https://arxiv.org/abs/2307.08065) \n* [TIST 2023] **Dual Graph Convolution Architecture Search for Travel Time Estimation** [(Paper)](https://dl.acm.org/doi/10.1145/3591361)\n* [WWW 2023] **Automated Self-Supervised Learning for Recommendation** [(Paper)](https://dl.acm.org/doi/10.1145/3543507.3583336)\n\n## Miscellaneous\n### Self-supervised Learning\n* [LOGS 2022] **AutoGDA Automated Graph Data Augmentation for Node Classification** [(Paper)](https://proceedings.mlr.press/v198/zhao22a.html)\n* [ICLR 2022] **Automated Self-Supervised Learning for Graphs** [(Paper)](https://arxiv.org/pdf/2106.05470.pdf) [(Code)](https://github.com/ChandlerBang/AutoSSL)\n* [AAAI 2022] **AutoGCL Automated Graph Contrastive Learning via Learnable View Generators** [(Paper)](https://arxiv.org/abs/2109.10259) [(Code)](https://github.com/Somedaywilldo/AutoGCL)\n* [ICML 2021] **Graph Contrastive Learning Automated** [(Paper)](https://arxiv.org/abs/2106.07594) [(Code)](https://github.com/Shen-Lab/GraphCL_Automated) \n### Others\n* [ICLR 2023] **AutoTransfer AutoML with Knowledge Transfer - An Application to Graph Neural Networks** [(Paper)](https://openreview.net/forum?id=yT8twuGdqCX)\n* [arXiv 2022] **AutoGML Fast Automatic Model Selection for Graph Machine Learning** [(Paper)](https://arxiv.org/abs/2206.09280)\n* [TKDE 2021] **Automated Unsupervised Graph Representation Learning** [(Paper)](https://ieeexplore.ieee.org/document/9547743/) [(Code)](https://drive.google.com/drive/folders/1F7_LWvEg9Z70OxW2YJmivzg3qJ7tC6mE)\n* [arXiv 2022] **Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020** [(Paper)](https://arxiv.org/abs/2204.02625)\n \n## Cite\n\nPlease consider citing our [survey paper](http://arxiv.org/abs/2103.00742) if you find this repository helpful:\n```\n@inproceedings{zhang2021automated,\n  title={Automated Machine Learning on Graphs: A Survey},\n  author={Zhang, Ziwei and Wang, Xin and Zhu, Wenwu},\n  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},\n  year={2021},\n  note={Survey track}\n}\n```\n \n \n\u003c!--\nhide not very related papers\n### Structure Learning\n* [ICPR 2020] **AOAM Automatic Optimization of Adjacency Matrix for Graph Convolutional Network** [(Paper)](https://ieeexplore.ieee.org/document/9412046/) [(Code)](https://github.com/xshura/AOAM)\n### Explanation\n* [ICML 2021 XAI workshop] **Towards Automated Evaluation of Explanations in Graph Neural Networks** [(Paper)](https://arxiv.org/abs/2106.11864) \n--\u003e\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/thumnlab%2Fawesome-auto-graph-learning/projects"}