mlsanityData to 7 Oct 2026

Synthetic Graph Generation to Benchmark Graph Learning

arXiv 2204.01376 ↗First posted 4 Apr 2022cs.LG cs.SIcited by 16

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Abstract (arXiv)

Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track the field's burgeoning progress. One reason is due to the very small number of datasets used in practice to benchmark the performance of graph learning algorithms. This shockingly small sample size (~10) allows for only limited scientific insight into the problem. In this work, we aim to address this deficiency. We propose to generate synthetic graphs, and study the behaviour of graph learning algorithms in a controlled scenario. We develop a fully-featured synthetic graph generator that allows deep inspection of different models. We argue that synthetic graph generations allows for thorough investigation of algorithms and provides more insights than overfitting on three citation datasets. In the case study, we show how our framework provides insight into unsupervised and supervised graph neural network models.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

20 of 28 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  7. 2018Deep Graph Infomaxcited by 1,101
  8. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  9. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  10. 2020Benchmarking Graph Neural Networkscited by 672
  11. 2020Can Graph Neural Networks Count Substructures?cited by 235
  12. 2020Design Space for Graph Neural Networkscited by 182
8 more
  1. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  2. 2020Graph Clustering with Graph Neural Networkscited by 163
  3. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  4. 2016Community detection in networks: A user guidecited by 105
  5. 2019A Review of Stochastic Block Models and Extensions for Graph Clusteringcited by 46
  6. 2020Grale: Designing Networks for Graph Learningcited by 28
  7. 2020Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designscited by 25
  8. 2020Pathfinder Discovery Networks for Neural Message Passingcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 7; 2025: 4; 2026: 1.

16 papers.

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  1. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 83
  2. 2025Edge Prompt Tuning for Graph Neural Networkscited by 18
  3. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  4. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  5. 2024GraphFM: A generalist graph transformer that learns transferable representations across diverse domainscited by 14
  6. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  7. 2022ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splittingcited by 13
  8. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 8
  9. 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishabilitycited by 7
  10. 2023Neural-prior stochastic block modelcited by 4
  11. 2024Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNscited by 4
  12. 2025Community detection robustness of graph neural networkscited by 1
4 more
  1. 2024SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanationcited by 0
  2. 2024Unsupervised Optimisation of GNNs for Node Clusteringcited by 0
  3. 2025Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Studycited by 0
  4. 2026Community Concealment from Graph Neural Networkscited by 0