mlsanityData to 7 Oct 2026

Everything is Connected: Graph Neural Networks

arXiv 2301.08210 ↗First posted 19 Jan 2023cs.LG cs.AI cs.SIcited by 85

Not on the map. Why?

Abstract (arXiv)

In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.

Key facts. Cited by 85 papers in the map (data through 7 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 19 Jan 2023.

Builds on

39 of 62 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2018How Powerful are Graph Neural Networks?cited by 4,355
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  7. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  8. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  9. 2019Simplifying Graph Convolutional Networkscited by 1,470
  10. 2017Deep Setscited by 1,394
  11. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  12. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
27 more
  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021E(n) Equivariant Graph Neural Networkscited by 951
  3. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  4. 2021How Attentive are Graph Attention Networks?cited by 818
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  6. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  7. 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 624
  8. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  9. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  10. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  11. 2018Neural Relational Inference for Interacting Systemscited by 339
  12. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  13. 2019What graph neural networks cannot learn: depth vs widthcited by 195
  14. 2020The Hardware Lotterycited by 160
  15. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 143
  16. 2017Predicting multicellular function through multi-layer tissue networkscited by 138
  17. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  18. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  19. 2019On the Limitations of Representing Functions on Setscited by 89
  20. 2020Differentiable Graph Module (DGM) for Graph Convolutional Networkscited by 86
  21. 2022Expander Graph Propagationcited by 75
  22. 2020Learning Graph Models for Retrosynthesis Predictioncited by 64
  23. 2020Pointer Graph Networkscited by 52
  24. 2022Message passing all the way upcited by 47
  25. 2020Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19cited by 38
  26. 2021Conditional Neural Relational Inference for Interacting Systemscited by 28
  27. 2019Drug-Drug Adverse Effect Prediction with Graph Co-Attentioncited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 22; 2024: 30; 2025: 25; 2026: 8.

85 papers.

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By area:

  1. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  2. 2023How does over-squashing affect the power of GNNs?cited by 53
  3. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  4. 2023TacticAI: an AI assistant for football tacticscited by 43
  5. 2024Position: Categorical Deep Learning is an Algebraic Theory of All Architecturescited by 32
  6. 2024Graph Transformers: A Surveycited by 32
  7. 2024Kolmogorov-Arnold Graph Neural Networkscited by 31
  8. 2023Graph-level Representation Learning with Joint-Embedding Predictive Architecturescited by 21
  9. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  10. 2023Neural incomplete factorization: learning preconditioners for the conjugate gradient methodcited by 18
  11. 2023Simplicial Representation Learning with Neural k-Formscited by 18
  12. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
38 more
  1. 2025Improving Equivariant Networks with Probabilistic Symmetry Breakingcited by 15
  2. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  3. 2023Asynchronous Algorithmic Alignment with Cocyclescited by 13
  4. 2024Benchmarking Positional Encodings for GNNs and Graph Transformerscited by 13
  5. 2025Machine learning for modelling unstructured grid data in computational physics: a reviewcited by 12
  6. 2025Towards Mechanistic Interpretability of Graph Transformers via Attention Graphscited by 12
  7. 2024Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Processcited by 11
  8. 2023TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformerscited by 9
  9. 2023Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Searchcited by 8
  10. 2024Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matchingcited by 8
  11. 2023A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networkscited by 6
  12. 2024Recurrent Aggregators in Neural Algorithmic Reasoningcited by 6
  13. 2024Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transformscited by 6
  14. 2023GPS++: Reviving the Art of Message Passing for Molecular Property Predictioncited by 5
  15. 2026Billion-Scale Graph Foundation Modelscited by 5
  16. 2023SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detectioncited by 4
  17. 2023iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservationcited by 4
  18. 2024Graph Neural Networks on Quantum Computerscited by 4
  19. 2024Topology meets Machine Learning: An Introduction using the Euler Characteristic Transformcited by 4
  20. 2024Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physicscited by 4
  21. 2023Causal Temporal Graph Convolutional Neural Networks (CTGCN)cited by 3
  22. 2025DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modelingcited by 3
  23. 2025Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualizationcited by 3
  24. 2025Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scanscited by 3
  25. 2023Marginal Nodes Matter: Towards Structure Fairness in Graphscited by 2
  26. 2023EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classificationcited by 2
  27. 2024Graph Transformers without Positional Encodingscited by 2
  28. 2024Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networkscited by 2
  29. 2024ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networkscited by 2
  30. 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learningcited by 2
  31. 2024Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNscited by 2
  32. 2024Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processingcited by 2
  33. 2024Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learningcited by 2
  34. 2025Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scanscited by 2
  35. 2026Have Graph -- Will Lift? The Case for Higher-Order Benchmarkscited by 2
  36. 2023A Model-Agnostic Graph Neural Network for Integrating Local and Global Informationcited by 1
  37. 2023Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirementscited by 1
  38. 2024A unifying primary framework for quantum graph neural networks from quantum graph statescited by 1

50 most cited of 85 All 85 (JSON)