mlsanityData to 7 Oct 2026

Search to aggregate neighborhood for graph neural network

arXiv 2104.06608 ↗First posted 14 Apr 2021cs.LGcited by 48

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Abstract (arXiv)

Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural architecture search (NAS), which has made impressive success in discovering effective architectures in convolutional neural networks. However, it is non-trivial to apply NAS approaches to GNN due to challenges in search space design and the expensive searching cost of existing NAS methods. In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures. By designing a novel and expressive search space, we propose a differentiable search algorithm, which is more efficient than previous reinforcement learning based methods. Experimental results on four tasks and seven real-world datasets demonstrate the superiority of SANE compared to existing GNN models and NAS approaches in terms of effectiveness and efficiency. (Code is available at: https://github.com/AutoML-4Paradigm/SANE).

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Builds on 41 of its 61 references that are in the map. First posted on arXiv 14 Apr 2021.

Builds on

41 of 61 references.

References · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2018How Powerful are Graph Neural Networks?cited by 4,355
  8. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  10. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  11. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  12. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
29 more
  1. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  4. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  5. 2020Simple and Deep Graph Convolutional Networkscited by 857
  6. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  7. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  8. 2019Heterogeneous Graph Attention Networkcited by 732
  9. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  10. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  11. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  12. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  13. 2018SNAS: Stochastic Neural Architecture Searchcited by 288
  14. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  15. 2017Cross-lingual Entity Alignment via Joint Attribute-Preserving Embeddingcited by 126
  16. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  17. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  18. 2019Multi-Channel Graph Neural Network for Entity Alignmentcited by 86
  19. 2019Auto-GNN: Neural Architecture Search of Graph Neural Networkscited by 73
  20. 2019Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searchingcited by 70
  21. 2019Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Networkcited by 68
  22. 2019BayesNAS: A Bayesian Approach for Neural Architecture Searchcited by 67
  23. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  24. 2020Policy-GNN: Aggregation Optimization for Graph Neural Networkscited by 48
  25. 2019Efficient Neural Architecture Search via Proximal Iterationscited by 31
  26. 2020Simplifying Architecture Search for Graph Neural Networkcited by 27
  27. 2019Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Searchcited by 23
  28. 2019Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfactioncited by 14
  29. 2020Price-aware Recommendation with Graph Convolutional Networkscited by 14

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Cited by year

Citing papers per year: 2021: 6; 2022: 13; 2023: 14; 2024: 11; 2025: 4.

48 papers.

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  1. 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Datacited by 71
  2. 2022A Survey on Graph Representation Learning Methodscited by 66
  3. 2021Pooling Architecture Search for Graph Classificationcited by 36
  4. 2021A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphscited by 26
  5. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  6. 2023Auto-HeG: Automated Graph Neural Network on Heterophilic Graphscited by 23
  7. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  8. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  9. 2021AutoGEL: An Automated Graph Neural Network with Explicit Link Informationcited by 17
  10. 2021Hierarchical Heterogeneous Graph Representation Learning for Short Text Classificationcited by 13
  11. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 13
  12. 2023Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Taskscited by 13
36 more
  1. 2023Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learningcited by 9
  2. 2023GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropycited by 9
  3. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  4. 2023AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networkscited by 8
  5. 2024Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modelingcited by 8
  6. 2023Search to Capture Long-range Dependency with Stacking GNNs for Graph Classificationcited by 7
  7. 2023Adversarially Robust Neural Architecture Search for Graph Neural Networkscited by 7
  8. 2023A Versatile Graph Learning Approach through LLM-based Agentcited by 6
  9. 2022Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networkscited by 5
  10. 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networkscited by 5
  11. 2024Unsupervised Graph Neural Architecture Search with Disentangled Self-supervisioncited by 5
  12. 2024Combinatorial Optimization with Automated Graph Neural Networkscited by 5
  13. 2022A Comprehensive Survey on Automated Machine Learning for Recommendationscited by 4
  14. 2022A Survey on Dropout Methods and Experimental Verification in Recommendationcited by 4
  15. 2024Calibrated Dataset Condensation for Faster Hyperparameter Searchcited by 4
  16. 2024Automatic Graph Topology-Aware Transformercited by 4
  17. 2024Customized Subgraph Selection and Encoding for Drug-drug Interaction Predictioncited by 4
  18. 2021Learn Layer-wise Connections in Graph Neural Networkscited by 3
  19. 2023Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Searchcited by 3
  20. 2024Knowledge-aware Evolutionary Graph Neural Architecture Searchcited by 3
  21. 2022Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Searchcited by 2
  22. 2022Search to Pass Messages for Temporal Knowledge Graph Completioncited by 2
  23. 2022Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approachcited by 2
  24. 2024Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsificationcited by 2
  25. 2024Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributescited by 2
  26. 2022Graph Neural Networks for Double-Strand DNA Breaks Predictioncited by 1
  27. 2022DFG-NAS: Deep and Flexible Graph Neural Architecture Searchcited by 1
  28. 2023Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networkscited by 1
  29. 2023Graph Neural Architecture Search with GPT-4cited by 1
  30. 2024Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Modelscited by 1
  31. 2024A Graph Neural Architecture Search Approach for Identifying Bots in Social Mediacited by 1
  32. 2025ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learningcited by 1
  33. 2025Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolutioncited by 1
  34. 2022Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020cited by 0
  35. 2025Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Designcited by 0
  36. 2025DARTS-GT: Differentiable Architecture Search for Graph Transformers with Quantifiable Instance-Specific Interpretability Analysiscited by 0