mlsanityData to 7 Oct 2026

A Gentle Introduction to Deep Learning for Graphs

arXiv 1912.12693 ↗First posted 29 Dec 2019cs.LG cs.SI stat.MLcited by 107

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Abstract (arXiv)

The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Dec 2019.

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Citing papers per year: 2021: 22; 2022: 18; 2023: 29; 2024: 18; 2025: 14; 2026: 6.

107 papers.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  3. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  4. 2022Anti-Symmetric DGN: a stable architecture for Deep Graph Networkscited by 58
  5. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  6. 2022Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentialscited by 37
  7. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  8. 2021GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networkscited by 35
  9. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  10. 2021MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networkscited by 32
  11. 2024Graph Transformers: A Surveycited by 32
  12. 2023Graph Deep Learning for Time Series Forecastingcited by 26
38 more
  1. 2024Long Range Propagation on Continuous-Time Dynamic Graphscited by 26
  2. 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 23
  3. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  4. 2023Dual Algorithmic Reasoningcited by 21
  5. 2021Power Flow Balancing with Decentralized Graph Neural Networkscited by 19
  6. 2023Neural Algorithmic Reasoning for Combinatorial Optimisationcited by 19
  7. 2023Deep learning for dynamic graphs: models and benchmarkscited by 19
  8. 2025Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learningcited by 18
  9. 2023Graph-based Time Series Clustering for End-to-End Hierarchical Forecastingcited by 16
  10. 2021Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spacescited by 15
  11. 2021A Meta-Learning Approach for Training Explainable Graph Neural Networkscited by 15
  12. 2022Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophilycited by 15
  13. 2022Leave Graphs Alone: Addressing Over-Squashing without Rewiringcited by 14
  14. 2024Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNscited by 14
  15. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  16. 2021Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classificationcited by 13
  17. 2024Deep Learning for Satellite Image Time Series Analysis: A Reviewcited by 13
  18. 2024Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passingcited by 13
  19. 2021Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolationcited by 11
  20. 2024Continual Learning on Graphs: A Surveycited by 11
  21. 2024Temporal Graph ODEs for Irregularly-Sampled Time Seriescited by 11
  22. 2025Relational Conformal Prediction for Correlated Time Seriescited by 11
  23. 2021Pyramidal Reservoir Graph Neural Networkcited by 10
  24. 2021Estimating the State of Epidemics Spreading with Graph Neural Networkscited by 9
  25. 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generationcited by 9
  26. 2022A Survey on Hyperlink Predictioncited by 9
  27. 2021Graph Neural Networks for Graph Drawingcited by 8
  28. 2022Towards Generalizable Graph Contrastive Learning: An Information Theory Perspectivecited by 8
  29. 2024Graph-based Virtual Sensing from Sparse and Partial Multivariate Observationscited by 8
  30. 2022NeuralNEB -- Neural Networks can find Reaction Paths Fastcited by 7
  31. 2023Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networkscited by 7
  32. 2023Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systemscited by 7
  33. 2021Dynamic Graph Echo State Networkscited by 6
  34. 2022Generalizing Downsampling from Regular Data to Graphscited by 6
  35. 2024Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalizationcited by 6
  36. 2021Graph neural network-based fault diagnosis: a reviewcited by 5
  37. 2022Leveraging Relational Information for Learning Weakly Disentangled Representationscited by 5
  38. 2023Graph Kalman Filterscited by 5

50 most cited of 107 All 107 (JSON)