2026 edition · Top 30
Graph learning
Learning on graphs and relational data: embeddings, graph neural networks, geometric deep learning.
Graph learning: 30 · N = 30, scaled from 78 candidates.
0 of 30 of this page's 30 read · saved in this browser only
- 1
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf · 37,775 citations · Papers With Code
Editor's note - 2
Neural Message Passing for Quantum Chemistry
Justin Gilmer · 9,719 citations · Papers With Code
Editor's note - 3
Inductive Representation Learning on Large Graphs
William L. Hamilton · 21,594 citations · Papers With Code
Editor's note - 4
DeepWalk: Online Learning of Social Representations
Bryan Perozzi · 11,046 citations · Papers With Code
Award - 5
- 6
Graph Attention Networks
Petar Veličković · 28,961 citations · Papers With Code
- 7
The Graph Neural Network Model
Franco Scarselli · 9,578 citations · OpenAlex
- 8
Translating Embeddings for Modeling Multi-relational Data
Antoine Bordes · 9,416 citations · Semantic Scholar
- 9
node2vec: Scalable Feature Learning for Networks
Aditya Grover · 12,705 citations · Papers With Code
- 10
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard · 8,971 citations · Semantic Scholar
- 11
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia · 3,800 citations · Papers With Code
- 12
The PageRank Citation Ranking: Bringing Order to the Web
Lawrence Page · 17,107 citations · Semantic Scholar
- 13
Spectral Networks and Locally Connected Networks on Graphs
Joan Bruna · 5,527 citations · Semantic Scholar
- 14
E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras · 1,705 citations · Papers With Code
- 15
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu · 3,981 citations · Papers With Code
- 16
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying · 4,334 citations · Papers With Code
- 17
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein · 1,797 citations · Papers With Code
- 18
Simplifying Graph Convolutional Networks
Felix Wu · 4,057 citations · Papers With Code
- 19
Gated Graph Sequence Neural Networks
Yujia Li · 3,735 citations · Semantic Scholar
- 20
Collective dynamics of ‘small-world’ networks
Duncan J. Watts · 43,059 citations · OpenAlex
- 21
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull · 6,465 citations · Papers With Code
- 22
Emergence of Scaling in Random Networks
Albert-László Barabási · 36,148 citations · OpenAlex
- 23
Graph Contrastive Learning with Augmentations
Yuning You · 3,054 citations · Papers With Code
- 24
Deep Graph Infomax
Petar Veličković · 3,183 citations · Papers With Code
- 25
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying · 2,640 citations · Semantic Scholar
- 26
Group Equivariant Convolutional Networks
Taco S. Cohen · 2,770 citations · Semantic Scholar
- 27
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Mikhail Belkin · 7,640 citations · OpenAlex
- 28
Deep Sets
Manzil Zaheer · 3,311 citations · Papers With Code
- 29
Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud · 3,719 citations · Papers With Code
- 30
How Attentive Are Graph Attention Networks?
2,155 citations · Papers With Code
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Sergey Brin · 17,919 citations · Semantic Scholar
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Remi Lam · 1,463 citations · OpenAlex
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Charles R. Qi · 18,771 citations · Papers With Code
Authoritative Sources in a Hyperlinked Environment
Jon M. Kleinberg · 9,032 citations · OpenAlex
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He · 5,834 citations · Papers With Code
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt · 1,559 citations · Semantic Scholar
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and
Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.