mlsanityData to 7 Oct 2026

Search For Deep Graph Neural Networks

arXiv 2109.10047 ↗First posted 21 Sep 2021cs.LG cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which aims at automatically generating high-performance while transferable deep GNN models in a block-wise manner. Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple flexible residual connection in our search space and apply identity mapping in the basic GNN layers. For the search algorithm, we use deep-q-learning with epsilon-greedy exploration strategy and reward reshaping. Extensive experiments on real-world datasets show that our generated GNN models outperforms existing manually designed and NAS-based ones.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 55 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

37 of 55 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  7. 2018How Powerful are Graph Neural Networks?cited by 4,355
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  10. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  11. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  12. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
25 more
  1. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  2. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  3. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  4. 2020Simple and Deep Graph Convolutional Networkscited by 857
  5. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  6. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  7. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  8. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  9. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  10. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  11. 2017Progressive Neural Architecture Searchcited by 483
  12. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  13. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  14. 2018Graph networks as learnable physics engines for inference and controlcited by 242
  15. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  16. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  17. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  18. 2018Jointly Multiple Events Extraction via Attention-based Graph Information Aggregationcited by 73
  19. 2019Auto-GNN: Neural Architecture Search of Graph Neural Networkscited by 73
  20. 2019Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networkscited by 62
  21. 2017A Genetic Programming Approach to Designing Convolutional Neural Network Architecturescited by 59
  22. 2020Understanding and Resolving Performance Degradation in Graph Convolutional Networkscited by 53
  23. 2019XNAS: Neural Architecture Search with Expert Advicecited by 35
  24. 2020Simplifying Architecture Search for Graph Neural Networkcited by 27
  25. 2017Conversation Modeling on Reddit using a Graph-Structured LSTMcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

6 papers.

  1. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  2. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  3. 2023Search to Capture Long-range Dependency with Stacking GNNs for Graph Classificationcited by 7
  4. 2025Deal: Distributed End-to-End GNN Inference for All Nodescited by 1
  5. 2022Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020cited by 0
  6. 2026AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networkscited by 0