Data to 14 Oct 2026

Representation Learning on Graphs: Methods and Applications

arXiv 1709.05584 ↗First posted 17 Sep 2017cs.SI cs.LGcited by 457

Not on the map. Why?

Abstract (arXiv)

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.

Key facts. Cited by 457 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 146; 2022: 109; 2023: 69; 2024: 62; 2025: 50; 2026: 21.

457 papers.

Sort:

By area:

2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 193
2021GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graphcited by 143
2021Global Self-Attention as a Replacement for Graph Convolutioncited by 114
2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 108
2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 82
2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81

38 more

2021Graph Representation Learning in Biomedicinecited by 80
2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2021Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platformcited by 66
2022A Survey on Graph Representation Learning Methodscited by 66
2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 66
2022DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detectioncited by 63
2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
2022Theory of Graph Neural Networks: Representation and Learningcited by 61
2021A Survey on Embedding Dynamic Graphscited by 60
2022Does GNN Pretraining Help Molecular Representation?cited by 60
2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
2024GKAN: Graph Kolmogorov-Arnold Networkscited by 53
2022A Short Tutorial on The Weisfeiler-Lehman Test And Its Variantscited by 52
2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
2023Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusioncited by 40
2021Automated Machine Learning on Graphs: A Surveycited by 39
2021Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networkscited by 39
2021A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspectivecited by 38
2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 38
2023Simple and Asymmetric Graph Contrastive Learning without Augmentationscited by 38
2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
2023Graph Positional and Structural Encodercited by 33
2021ReGVD: Revisiting Graph Neural Networks for Vulnerability Detectioncited by 32
2022FP-GNN: a versatile deep learning architecture for enhanced molecular property predictioncited by 31
2022Metropolis-Hastings Data Augmentation for Graph Neural Networkscited by 30
2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
2021Marius: Learning Massive Graph Embeddings on a Single Machinecited by 29
2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
2022Graph Neural Networks with Adaptive Readoutscited by 29
2021Fair Representation Learning for Heterogeneous Information Networkscited by 28
2022MGAE: Masked Autoencoders for Self-Supervised Learning on Graphscited by 28
2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28

50 most cited of 457 All 457 (JSON)