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A paper collection about automated graph learning

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awesome-auto-graph-learning

This 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).

Please submit a pull request if you want to add new papers or have any suggestions!

Survey

  • [IJCAI 2021] Automated Machine Learning on Graphs: A Survey (Paper)
  • [Extension] Automated Graph Machine Learning: Approaches, Libraries and Directions (Paper)

Tool

Graph NAS

2022

  • [NeurIPS 2022] NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search (Paper)(Code)
  • [CIKM 2022] GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search (Paper) (Code)
  • [ICML 2022] Large-Scale Graph Neural Architecture Search (Paper) (Code)
  • [ICML 2022] Graph Neural Architecture Search Under Distribution Shifts (Paper)
  • [ICML 2022] DFG-NAS: Deep and Flexible Graph Neural Architecture Search (Paper) (Code)
  • [KDD 2022] Graph Neural Networks with Node-wise Architecture (Paper)
  • [KDDDLG 2022] Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search (Paper) (Code)
  • [SIGIR 2022] AutoGSR: Neural Architecture Search for Graph-based Session Recommendation (Paper)
  • [TKDE 2022] GraphNAS++: Distributed Architecture Search for Graph Neural Network (Paper)
  • [CVPR 2022] Automatic Relation-aware Graph Network Proliferation (Paper) (Code)
  • [WWW 2022] PaSca a Graph Neural Architecture Search System under the Scalable Paradigm (Paper) (Code)
  • [WWW 2022] Designing the Topology of Graph Neural Networks A Novel Feature Fusion Perspective (Paper) (Code)
  • [ICDE 2022] AutoHEnsGNN Winning Solution to AutoGraph Challenge for KDD Cup 2020 (Paper) (Code)
  • [TPDS 2022] Auto-GNAS A Parallel Graph Neural Architecture Search Framework (Paper)
  • [WSDM 2022] Profiling the Design Space for Graph Neural Networks based Collaborative Filtering (Paper) (Code)
  • [Applied Intelligence] Automatic search of architecture and hyperparameters of graph convolutional networks for node classification (Paper)
  • [arXiv 2022] AutoKE: An automatic knowledge embedding framework for scientific machine learning (Paper)

2021

  • [NeurIPS 2021] Graph Differentiable Architecture Search with Structure Learning (Paper) (Code)
  • [NeurIPS 2021] AutoGEL: An Automated Graph Neural Network with Explicit Link Information (Paper) (Code)
  • [ICDM 2021] Heterogeneous Graph Neural Architecture Search (Paper)
  • [IJCNN 2021] Automated Graph Representation Learning for Node Classification (Paper)
  • [PRICAI 2021] ALGNN Auto-Designed Lightweight Graph Neural Network (Paper)
  • [CIKM 2021] Pooling Architecture Search for Graph Classification (Paper) (Code)
  • [KDD 2021] DiffMG Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks (Paper) (Code)
  • [KDD 2021 DLG Workshop] Learn Layer-wise Connections in Graph Neural Networks (Paper)
  • [ICML 2021] AutoAttend Automated Attention Representation Search (Paper)
  • [SIGIR 2021] GraphPAS Parallel Architecture Search for Graph Neural Networks (Paper)
  • [CVPR 2021] Rethinking Graph Neural Network Search from Message-passing (Paper) (Code)
  • [GECCO 2021] Fitness Landscape Analysis of Graph Neural Network Architecture Search Spaces (Paper) (Code)
  • [EuroSys 2021 EuroMLSys workshop] Learned low precision graph neural networks (Paper)
  • [WWW 2021] Autostg: Neural architecture search for predictions of spatio-temporal graphs (Paper) (Code)
  • [ICDE 2021] Search to aggregate neighborhood for graph neural network (Paper) (Code)
  • [AAAI 2021] One-shot graph neural architecture search with dynamic search space (Paper)
  • [arXiv] Search For Deep Graph Neural Networks (Paper)
  • [arXiv] G-CoS GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency (Paper)
  • [arXiv] Edge-featured Graph Neural Architecture Search (Paper)
  • [arXiv] FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search (Paper)

2020

  • [NeurIPS 2020] Design space for graph neural networks (Paper) (Code)
  • [ICONIP 2020] Autograph: Automated graph neural network (Paper)
  • [BigData 2020] Graph neural network architecture search for molecular property prediction (Paper) (Code)
  • [CIKM 2020] Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network (Paper) (Code)
  • [CIKM 2020 CSSA workshop] Simplifying architecture search for graph neural network(Paper) (Code)
  • [BRACIS 2020] Neural architecture search in graph neural networks (Paper) (Code)
  • [IJCAI 2020] Graph neural architecture search (Paper) (Code)
  • [CVPR 2020] SGAS: Sequential Greedy Architecture Search (Paper) (Code)
  • [AAAI 2020] Learning graph convolutional network for skeleton-based human action recognition by neural searching (Paper) (Code)
  • [OpenReview 2020] Efficient graph neural architecture search (Paper)
  • [OpenReview 2020] FGNAS: FPGA-Aware Graph Neural Architecture Search (Paper)
  • [arXiv 2020] Evolutionary architecture search for graph neural networks (Paper) (Code)
  • [arXiv 2020] Probabilistic dual network architecture search on graphs (Paper)

2019

  • [arXiv 2019] Auto-gnn: Neural architecture search of graph neural networks (Paper)

Graph HPO

2022

  • [CIKM 2022] Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty (Paper)
  • [KAIS 2022] Autonomous graph mining algorithm search with best performance trade-off (Paper)
  • [ACL 2022] KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning (Paper)
  • [arXiv 2022] Start Small, Think Big On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings (Paper)
  • [arXiv 2022] Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality (Paper)

2021

  • [ICML 2021] Explainable Automated Graph Representation Learning with Hyperparameter Importance (Paper)
  • [SIGIR 2021] Automated Graph Learning via Population Based Self-Tuning GCN (Paper)
  • [PRICAI 2021] Automatic Graph Learning with Evolutionary Algorithms: An Experimental Study (Paper)
  • [GECCO 2021] Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction (Paper)
  • [P2PNA 2021] ASFGNN Automated separated-federated graph neural network (Paper)
  • [arXiv 2021] A novel genetic algorithm with hierarchical evaluation strategy for hyperparameter optimisation of graph neural networks (Paper)
  • [arXiv 2021] Jitune: Just-in-time hyperparameter tuning for network embedding algorithms (Paper)

2020

  • [ICDM 2020] Autonomous graph mining algorithm search with best speed/accuracy trade-off (Paper) (Code)

2019

  • [KDD 2019] AutoNE: Hyperparameter optimization for massive network embedding (Paper) (Code)

Applications

Finance

  • [CIKM 2022] Explainable Graph-based Fraud Detection via Neural Meta-graph Search (Paper)

Biology

  • [TCBB 2022] Multi-view Graph Neural Architecture Search for Biomedical Entity and Relation Extraction (Paper)
  • [TCBB 2022] AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction (Paper)
  • [AILSCI 2022] AutoGGN: A gene graph network AutoML tool for multi-omics research (Paper)
  • [BIBM 2021] Multi-label Metabolic Pathway Prediction with Auto Molecular Structure Representation Learning (Paper)

Knowledge Graph Embedding

  • [arXiv 2021] AutoSF+: Towards Automatic Scoring Function Design for Knowledge Graph Embedding (Paper)
  • [ICDE 2020] AutoSF: Searching Scoring Functions for Knowledge Graph Embedding (Paper) (Code)

Miscellaneous

Self-supervised Learning

  • [ICLR 2022] Automated Self-Supervised Learning for Graphs (Paper) (Code)
  • [AAAI 2022] AutoGCL Automated Graph Contrastive Learning via Learnable View Generators (Paper) (Code)
  • [ICML 2021] Graph Contrastive Learning Automated (Paper) (Code)

Others

  • [arXiv 2022] AutoGML Fast Automatic Model Selection for Graph Machine Learning (Paper)
  • [TKDE 2021] Automated Unsupervised Graph Representation Learning (Paper) (Code)
  • [arXiv 2022] Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020 (Paper)

Cite

Please consider citing our survey paper if you find this repository helpful:

@inproceedings{zhang2021automated,
  title={Automated Machine Learning on Graphs: A Survey},
  author={Zhang, Ziwei and Wang, Xin and Zhu, Wenwu},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},
  year={2021},
  note={Survey track}
}

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A paper collection about automated graph learning

License:MIT License