mlsanityData to 7 Oct 2026

Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

arXiv 2007.02901 ↗First posted 6 Jul 2020cs.LG cs.SI stat.MLcited by 253

Not on the map. Why?

Abstract (arXiv)

We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding to Computer Science articles, with edges based on hyperlinks and 10 classes representing different branches of the field. We use the dataset to evaluate semi-supervised node classification and single-relation link prediction models. Our experiments show that these methods perform well on a new domain, with structural properties different from earlier benchmarks. The dataset is publicly available, along with the implementation of the data pipeline and the benchmark experiments, at https://github.com/pmernyei/wiki-cs-dataset .

Key facts. Cited by 253 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 33; 2023: 25; 2024: 52; 2025: 72; 2026: 48.

253 papers.

Sort:

By area:

  1. 2021Graph Self-Supervised Learning: A Surveycited by 289
  2. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  3. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  4. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  5. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  6. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  7. 2021MagNet: A Neural Network for Directed Graphscited by 104
  8. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  9. 2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
  10. 2021GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddingscited by 90
  11. 2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
  12. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 83
38 more
  1. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  2. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  3. 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Timecited by 64
  4. 2022Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminatingcited by 57
  5. 2024GOFA: A Generative One-For-All Model for Joint Graph Language Modelingcited by 56
  6. 2024Efficient Tuning and Inference for Large Language Models on Textual Graphscited by 54
  7. 2021Automated Self-Supervised Learning for Graphscited by 50
  8. 2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49
  9. 2022COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learningcited by 38
  10. 2022Spectral Augmentation for Self-Supervised Learning on Graphscited by 38
  11. 2021GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Predictioncited by 36
  12. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 36
  13. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
  14. 2021Graph Communal Contrastive Learningcited by 34
  15. 2025RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometrycited by 32
  16. 2025Turning Tabular Foundation Models into Graph Foundation Modelscited by 30
  17. 2023Learning on Graphs with Out-of-Distribution Nodescited by 29
  18. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  19. 2021Curvature Graph Neural Networkcited by 24
  20. 2023Class-Imbalanced Learning on Graphs: A Surveycited by 24
  21. 2023Towards Deep Attention in Graph Neural Networks: Problems and Remediescited by 24
  22. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  23. 2024Rethinking Graph Masked Autoencoders through Alignment and Uniformitycited by 22
  24. 2022Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representationscited by 21
  25. 2023GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstructioncited by 21
  26. 2024Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneckcited by 21
  27. 2025When Do LLMs Help With Node Classification? A Comprehensive Analysiscited by 21
  28. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 21
  29. 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 20
  30. 2022Compact Graph Structure Learning via Mutual Information Compressioncited by 20
  31. 2022Informative Pseudo-Labeling for Graph Neural Networks with Few Labelscited by 19
  32. 2022Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learningcited by 19
  33. 2024Learning Graph Quantized Tokenizerscited by 19
  34. 2022Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networkscited by 18
  35. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  36. 2023Continual Graph Learning: A Surveycited by 17
  37. 2024Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoningcited by 17
  38. 2022Characterizing the Influence of Graph Elementscited by 16

50 most cited of 253 All 253 (JSON)