mlsanityData to 7 Oct 2026

Should Graph Neural Networks Use Features, Edges, Or Both?

arXiv 2103.06857 ↗First posted 11 Mar 2021cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Graph Neural Networks (GNNs) are the first choice for learning algorithms on graph data. GNNs promise to integrate (i) node features as well as (ii) edge information in an end-to-end learning algorithm. How does this promise work out practically? In this paper, we study to what extend GNNs are necessary to solve prominent graph classification problems. We find that for graph classification, a GNN is not more than the sum of its parts. We also find that, unlike features, predictions with an edge-only model do not always transfer to GNNs.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 32 references that are in the map. First posted on arXiv 11 Mar 2021.

Builds on

26 of 32 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  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. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  8. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  9. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  10. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  11. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  12. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
14 more
  1. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  2. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  3. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  4. 2015Image-based Recommendations on Styles and Substitutescited by 702
  5. 2020Benchmarking Graph Neural Networkscited by 672
  6. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  7. 2019Graph U-Netscited by 489
  8. 2019Self-Attention Graph Poolingcited by 421
  9. 2016Learning Convolutional Neural Networks for Graphscited by 314
  10. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  11. 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
  12. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  13. 2018Towards Sparse Hierarchical Graph Classifierscited by 100
  14. 2019Dimensional Reweighting Graph Convolutional Networkscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

7 papers.

  1. 2021Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node featurescited by 9
  2. 2021LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphscited by 1
  3. 2022Learning Label Initialization for Time-Dependent Harmonic Extensioncited by 1
  4. 2023Predicting Properties of Nodes via Community-Aware Featurescited by 1
  5. 2024KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?cited by 1
  6. 2025Node Embeddings via Neighbor Embeddingscited by 1
  7. 2026Is One Token All It Takes? Graph Pooling Tokens for LLM-based GraphQAcited by 1