mlsanityData to 7 Oct 2026

Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks

arXiv 2105.08330 ↗First posted 18 May 2021cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline's test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%~2.84%. We open source our implementation at https://github.com/ytchx1999/PyG-OGB-Tricks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 18 May 2021.

Builds on

31 of 42 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2016Layer Normalizationcited by 5,579
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  10. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  11. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  12. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
19 more
  1. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  2. 2020Simple and Deep Graph Convolutional Networkscited by 857
  3. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  4. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  5. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  6. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  7. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  8. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  9. 2020Towards Deeper Graph Neural Networkscited by 296
  10. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  11. 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
  12. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  13. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  14. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  15. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  16. 2020Understanding and Resolving Performance Degradation in Graph Convolutional Networkscited by 53
  17. 2021Bag of Tricks for Node Classification with Graph Neural Networkscited by 40
  18. 2018Semi-supervised User Geolocation via Graph Convolutional Networkscited by 27
  19. 2016Column Networks for Collective Classificationcited by 17

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.

3 papers.

  1. 2021Scalable deeper graph neural networks for high-performance materials property predictioncited by 26
  2. 2022End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learningcited by 3
  3. 2025Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systemscited by 1