Awesome Graph Level Learning
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A collection of papers, implementations, datasets, and tools for graph-level learning.
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Awesome Graph-level Learning
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Surveys
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Traditional Graph-level Learning
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Graph Kernels
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Message Passing Kernels
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Shortest Path Kernels
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Random Walk Kernels
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Optimal Assignment Kernels
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Subgraph Kernels
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Subgraph Mining
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Frequent Subgraph Mining
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Discriminative Subgraph Mining
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Graph Embedding
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Deterministic Graph Embedding
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Learnable Graph Embedding
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Graph-Level Deep Neural Networks (GL-DNNs)
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Recurrent Neural Network-based Graph-level Learning
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Convolution Neural Network-based Graph-level Learning
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Capsule Neural Network-based Graph-level Learning
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Graph-Level Graph Neural Networks (GL-GNNs)
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Message Passing Neural Networks
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Subgraph-based GL-GNNs
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Kernel-based GL-GNNs
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Contrastive-based GL-GNNs
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Spectral-based GL-GNNs
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Graph Pooling
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Global Graph Pooling
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Numeric Operation Pooling
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Attention-based Pooling
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Convolution Neural Network-based Pooling
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Global Top-K Pooling
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Hierarchical Graph Pooling
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Clustering-based Pooling
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Hierarchical Top-K Pooling
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Hierarchical Tree-based Pooling
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Datasets
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Tools
A Timeline of Graph-level Learning
![timeline](https://github.com/ZhenyuYangMQ/Awesome-Graph-Level-Learning/raw/main/timeline.png)
Surveys
Paper Title |
Venue |
Year |
Materials |
A Comprehensive Survey of Graph-level Learning |
arXiv |
2023 |
[Paper] |
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities |
arXiv |
2022 |
[Paper] |
Graph-level Neural Networks: Current Progress and Future Directions |
arXiv |
2022 |
[Paper] |
A Survey on Graph Kernels |
Appl. Netw. Sci. |
2020 |
[Paper] |
Deep Learning on Graphs: A Survey |
IEEE Trans. Knowl. Data Eng. |
2020 |
[Paper] |
A Comprehensive Survey on Graph Neural Networks |
IEEE Trans. Neural Netw. Learn. Syst. |
2020 |
[Paper] |
Traditional Graph-level Learning
Graph Kernels
Message Passing Kernels
Paper Title |
Venue |
Year |
Method |
Materials |
A Persistent Weisfeiler-lehman Procedure for Graph Classification |
ICML |
2019 |
P-WL |
[Paper] [Code] |
Glocalized Weisfeiler-lehman Graph Kernels: Global-local Feature Maps of Graphs |
ICDM |
2017 |
Global-WL |
[Paper] [Code] |
Propagation kernels: Efficient Graph Kernels from Propagated Information |
Mach. Learn. |
2016 |
PK |
[Paper] [Code] |
Weisfeiler-lehman Graph Kernels |
J. Mach. Learn. Res. |
2011 |
WL |
[Paper] [Code] |
A linear-time graph kernel |
ICDM |
2009 |
NHK |
[Paper] [Code] |
Shortest Path Kernels
Paper Title |
Venue |
Year |
Method |
Materials |
Shortest-path Graph Kernels for Document Similarity |
EMNLP |
2017 |
SPK-DS |
[Paper] |
Shortest-path Kernels on Graphs |
ICDM |
2005 |
SPK |
[Paper] [Code] |
Random Walk Kernels
Paper Title |
Venue |
Year |
Method |
Materials |
Graph Kernels |
J. Mach. Learn. Res. |
2010 |
SOMRWK |
[Paper] [Code] |
Extensions of Marginalized Graph Kernels |
ICML |
2004 |
ERWK |
[Paper] [Code] |
On Graph Kernels: Hardness Results and Efficient Alternatives |
LNAI |
2003 |
RWK |
[Paper] [Code] |
Optimal Assignment Kernels
Paper Title |
Venue |
Year |
Method |
Materials |
Transitive Assignment Kernels for Structural Classification |
SIMBAD |
2015 |
TAK |
[Paper] |
Learning With Similarity Functions on Graphs Using Matchings of Geometric Embeddings |
KDD |
2015 |
GE-OAK |
[Paper] |
Solving the Multi-way Matching Problem by Permutation Synchronization |
NeurIPS |
2013 |
PS-OAK |
[Paper] [Code] |
Optimal Assignment Kernels for Attributed Molecular Graphs |
ICML |
2005 |
OAK |
[Paper] |
Subgraph Kernels
Paper Title |
Venue |
Year |
Method |
Materials |
Subgraph Matching Kernels for Attributed Graphs |
ICML |
2012 |
SMK |
[Paper] [Code] |
Fast Neighborhood Subgraph Pairwise Distance Kernel |
ICML |
2010 |
NSPDK |
[Paper] [Code] |
Efficient Graphlet Kernels for Large Graph Comparison |
AISTATS |
2009 |
Graphlet |
[Paper] [Code] |
Subgraph Mining
Frequent Subgraph Mining
Paper Title |
Venue |
Year |
Method |
Materials |
gspan: Graph-based Substructure Pattern Mining |
ICDM |
2002 |
gspan |
[Paper] [Code] |
Frequent Subgraph Discovery |
ICDM |
2001 |
FSG |
[Paper] [Code] |
An Apriori-based Algorithmfor Mining Frequent Substructures from Graph Data |
ECML-PKDD |
2000 |
AGM |
[Paper] [Code] |
Discriminative Subgraph Mining
Paper Title |
Venue |
Year |
Method |
Materials |
Multi-graph-view Learning for Graph Classification |
ICDM |
2014 |
gCGVFL |
[Paper] |
Positive and Unlabeled Learning for Graph Classification |
ICDM |
2011 |
gPU |
[Paper] |
Semi-supervised Feature Selection for Graph Classification |
KDD |
2010 |
gSSC |
[Paper] |
Multi-label Feature Selection for Graph Classification |
ICDM |
2010 |
gMLC |
[Paper] |
Near-optimal Supervised Feature Selection Among Frequent Subgraphs |
SDM |
2009 |
CORK |
[Paper] |
Mining Significant Graph Patterns by Leap Search |
SIGMOD |
2008 |
LEAP |
[Paper] |
Graph Embedding
Deterministic Graph Embedding
Paper Title |
Venue |
Year |
Method |
Materials |
Fast Attributed Graph Embedding via Density of States |
ICDM |
2021 |
A-DOGE |
[Paper] [Code] |
Bridging the Gap Between Von Neumann Graph Entropy and Structural Information: Theory and Applications |
WWW |
2021 |
VNGE |
[Paper] [Code] |
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs |
WWW |
2021 |
SLAQ |
[Paper] [Code] |
A Simple Yet Effective Baseline for Non-attributed Graph Classification |
ICLR-RLGM |
2019 |
LDP |
[Paper] [Code] |
Anonymous Walk Embeddings |
ICML |
2018 |
AWE |
[Paper] [Code] |
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs |
NeurIPS |
2017 |
FGSD |
[Paper] [Code] |
Learnable Graph Embedding
Paper Title |
Venue |
Year |
Method |
Materials |
Learning Graph Representation via Frequent Subgraphs |
SDM |
2018 |
GE-FSG |
[Paper] [Code] |
graph2vec: Learning Distributed Representations of Graphs |
KDD-MLG |
2017 |
graph2vec |
[Paper] [Code] |
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs |
KDD-MLG |
2016 |
subgraph2vec |
[Paper] [Code] |
Graph-Level Deep Neural Networks
Recurrent Neural Network-based Graph-level Learning
Paper Title |
Venue |
Year |
Method |
Materials |
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models |
ICML |
2018 |
GraphRNN |
[Paper] [Code] |
NetGAN: Generating Graphs via Random Walks |
ICML |
2018 |
NetGAN |
[Paper] [Code] |
Substructure Assembling Network for Graph Classification |
AAAI |
2018 |
SAN |
[Paper] |
Graph Classification using Structural Attention |
KDD |
2018 |
GAM |
[Paper] [Code] |
Gated Graph Sequence Neural Networks |
ICLR |
2016 |
GGNN |
[Paper] [Code] |
Convolution Neural Network-based Graph-level Learning
Paper Title |
Venue |
Year |
Method |
Materials |
Kernel Graph Convolutional Neural Networks |
ICANN |
2018 |
KCNN |
[Paper] [Code] |
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs |
CVPR |
2017 |
ECC |
[Paper] [Code] |
Diffusion-Convolutional Neural Networks |
NeurIPS |
2016 |
DCNN |
[Paper] [Code] |
Learning Convolutional Neural Networks for Graphs |
ICML |
2016 |
PATCHYSAN |
[Paper] [Code] |
Capsule Neural Network-based Graph-level Learning
Paper Title |
Venue |
Year |
Method |
Materials |
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations |
arXiv |
2019 |
PATCHYCaps |
[Paper] [Code] |
Capsule Graph Neural Network |
ICLR |
2019 |
CapsGNN |
[Paper] [Code] |
Graph Capsule Convolutional Neural Networks |
WCB |
2018 |
GCAPSCNN |
[Paper] [Code] |
Graph-Level Graph Neural Networks
Message Passing Neural Networks
Paper Title |
Venue |
Year |
Method |
Materials |
The Surprising Power of Graph Neural Networks with Random Node Initialization |
IJCAI |
2021 |
RNI |
[Paper] |
Weisfeiler and Lehman Go Cellular: CW Networks |
NeurIPS |
2021 |
CWN |
[Paper] [Code] |
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks |
ICML |
2021 |
SWL |
[Paper] [Code] |
Expressive Power of Invariant and Equivariant Graph Neural Networks |
ICLR |
2021 |
FGNN |
[Paper] [Code] |
Relational Pooling for Graph Representations |
ICML |
2019 |
RP |
[Paper] [Code] |
Provably Powerful Graph Networks |
NeurIPS |
2019 |
PPGN |
[Paper] [Code] |
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks |
AAAI |
2019 |
K-GNN |
[Paper] [Code] |
How Powerful are Graph Neural Networks? |
ICLR |
2019 |
GIN |
[Paper] [Code] |
Quantum-chemical Insights from Deep Tensor Neural Networks |
Nat. Commun. |
2017 |
DTNN |
[Paper] [Code] |
Neural Message Passing for Quantum Chemistry |
ICML |
2017 |
MPNN |
[Paper] [Code] |
Interaction Networks for Learning about Objects, Relations and Physics |
NeurIPS |
2016 |
GraphSim |
[Paper] [Code] |
Convolutional Networks on Graphs for Learning Molecular Fingerprints |
NeurIPS |
2015 |
Fingerprint |
[Paper] [Code] |
Subgraph-based GL-GNNs
Paper Title |
Venue |
Year |
Method |
Materials |
Equivariant Subgraph Aggregation Networks |
ICLR |
2021 |
ESAN |
[Paper] [Code] |
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism |
WWW |
2021 |
SUGAR |
[Paper] [Code] |
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" |
ICLR |
2021 |
GraphSNN |
[Paper] [Code] |
Nested Graph Neural Network |
NeurIPS |
2021 |
NGNN |
[Paper] [Code] |
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness |
ICLR |
2021 |
GNN-AK |
[Paper] [Code] |
Subgraph Neural Networks |
NeurIPS |
2020 |
SubGNN |
[Paper] [Code] |
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting |
IEEE Trans. Pattern Anal. Mach. Intell. |
2020 |
GSN |
[Paper] [Code] |
Kernel-based GL-GNNs
Paper Title |
Venue |
Year |
Method |
Materials |
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels |
WWW |
2021 |
GSKN |
[Paper] [Code] |
Random Walk Graph Neural Networks |
NeurIPS |
2020 |
RWNN |
[Paper] [Code] |
Convolutional Kernel Networks for Graph-Structured Data |
ICML |
2020 |
GCKN |
[Paper] [Code] |
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels |
WWW |
2019 |
DDGK |
[Paper] [Code] |
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels |
NeurIPS |
2019 |
GNTK |
[Paper] [Code] |
Contrastive-based GL-GNNs
Paper Title |
Venue |
Year |
Method |
Materials |
Graph Contrastive Learning Automated |
ICML |
2021 |
JOAO |
[Paper] [Code] |
Contrastive Multi-View Representation Learning on Graphs |
ICML |
2020 |
MVGRL |
[Paper] [Code] |
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training |
KDD |
2020 |
ESAN |
[Paper] [Code] |
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization |
ICLR |
2020 |
InfoGraph |
[Paper] [Code] |
Graph Contrastive Learning with Augmentations |
NeurIPS |
2020 |
GraphCL |
[Paper] [Code] |
Spectral-based GL-GNNs
Paper Title |
Venue |
Year |
Method |
Materials |
How Framelets Enhance Graph Neural Networks |
ICML |
2021 |
UFG |
[Paper] [Code] |
Graph Neural Networks With Convolutional ARMA Filters |
IEEE Trans. Pattern Anal. Mach. Intell. |
2021 |
ARMA |
[Paper] [Code] |
Breaking the Limits of Message Passing Graph Neural Networks |
ICML |
2021 |
GNNMatlang |
[Paper] [Code] |
Transferability of Spectral Graph Convolutional Neural Networks |
J. Mach. Learn. Res. |
2021 |
GNNTFS |
[Paper] |
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering |
NeurIPS |
2016 |
ChebNet |
[Paper] [Code] |
Graph Pooling
Global Graph Pooling
Numeric Operation Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
Second-Order Pooling for Graph Neural Networks |
IEEE Trans. Pattern Anal. Mach. Intell |
2020 |
SOPOOL |
[Paper] [Code] |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks |
ACL |
2020 |
TextING |
[Paper] [Code] |
Principal Neighbourhood Aggregation for Graph Nets |
NeurIPS |
2020 |
PNA |
[Paper] [Code] |
Attention-based Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
Order Matters: Sequence to Sequence for Sets |
ICLR |
2021 |
Set2Set |
[Paper] [Code] |
Convolution Neural Network-based Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
Kernel Graph Convolutional Neural Networks |
ICANN |
2018 |
KCNN |
[Paper] [Code] |
Learning Convolutional Neural Networks for Graphs |
ICML |
2016 |
PATCHYSAN |
[Paper] [Code] |
Global Top-K Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
Structure-Feature based Graph Self-adaptive Pooling |
WWW |
2020 |
GSAPool |
[Paper] [Code] |
An End-to-End Deep Learning Architecture for Graph Classification |
AAAI |
2018 |
SortPool |
[Paper] [Code] |
Hierarchical Graph Pooling
Clustering-based Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
Accurate Learning of Graph Representations with Graph Multiset Pooling |
ICLR |
2020 |
GMT |
[Paper] [Code] |
Spectral Clustering with Graph Neural Networks for Graph Pooling |
ICML |
2020 |
MinCutPool |
[Paper] [Code] |
StructPool: Structured Graph Pooling via Conditional Random Fields |
ICLR |
2020 |
StructPool |
[Paper] [Code] |
Graph Convolutional Networks with EigenPooling |
KDD |
2019 |
EigenPool |
[Paper] [Code] |
Hierarchical Graph Representation Learning with Differentiable Pooling |
NeurIPS |
2018 |
DiffPool |
[Paper] [Code] |
Deep Convolutional Networks on Graph-Structured Data |
arXiv |
2015 |
GraphCNN |
[Paper] [Code] |
Spectral Networks and Locally Connected Networks on Graphs |
ICLR |
2014 |
DLCN |
[Paper] |
Hierarchical Top-K Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations |
AAAI |
2020 |
ASAP |
[Paper] [Code] |
Self-Attention Graph Pooling |
ICML |
2019 |
SAGPool |
[Paper] [Code] |
Graph U-Nets |
ICML |
2019 |
U-Nets |
[Paper] [Code] |
Towards Sparse Hierarchical Graph Classifiers |
arXiv |
2018 |
SHGC |
[Paper] [Code] |
Hierarchical Tree-based Pooling
Paper Title |
Venue |
Year |
Method |
Materials |
A Simple yet Effective Method for Graph Classification |
IJCAI |
2022 |
HRN |
[Paper] [Code] |
Edge Contraction Pooling for Graph Neural Networks |
arXiv |
2019 |
EdgePool |
[Paper] [Code] |
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs |
CVPR |
2017 |
MoNet |
[Paper] [Code] |
Datasets
Biology
Dataset |
Size |
Graphs |
Classes |
Link |
ENZYMES |
Small |
600 |
6 |
Link |
PROTEINS |
Small |
1113 |
2 |
Link |
D&D |
Small |
1178 |
2 |
Link |
BACE |
Small |
1513 |
2 |
Link |
MUV |
Medium |
93087 |
2 |
Link |
ppa |
Medium |
158100 |
37 |
Link |
Chemistry
Dataset |
Size |
Graphs |
Classes |
Link |
MUTAG |
Small |
188 |
2 |
Link |
SIDER |
Small |
1427 |
2 |
Link |
ClinTox |
Small |
1477 |
2 |
Link |
BBBP |
Small |
2039 |
2 |
Link |
Tox21 |
Small |
7831 |
2 |
Link |
ToxCast |
Small |
8576 |
2 |
Link |
molhiv |
Small |
41127 |
2 |
Link |
molpcba |
Medium |
437929 |
2 |
Link |
FreeSolv |
Small |
642 |
- |
Link |
ESOL |
Small |
1128 |
- |
Link |
Lipophilicity |
Small |
4200 |
- |
Link |
AQSOL |
Small |
9823 |
- |
Link |
ZINC |
Small |
12000 |
- |
Link |
QM9 |
Medium |
129433 |
- |
Link |
Social Networks
Dataset |
Size |
Graphs |
Classes |
Link |
IMDB-BINARY |
Small |
1000 |
2 |
Link |
IMDB-MULTI |
Small |
1500 |
3 |
Link |
DBLP_v1 |
Small |
19456 |
2 |
Link |
COLLAB |
Medium |
5000 |
3 |
Link |
REDDIT-BINARY |
Small |
2000 |
2 |
Link |
REDDIT-MULTI-5K |
Medium |
4999 |
5 |
Link |
REDDIT-MULTI-12K |
Medium |
11929 |
11 |
Link |
Computer Science
Dataset |
Size |
Graphs |
Classes |
Link |
CIFAR10 |
Medium |
60000 |
10 |
Link |
MNIST |
Medium |
70000 |
10 |
Link |
code2 |
Medium |
452741 |
- |
Link |
MALNET |
Large |
1262024 |
696 |
Link |
Dataset Library
- TUDataset https://chrsmrrs.github.io/datasets/docs/datasets/
- MoleculeNetDataset https://moleculenet.org/datasets-1
- OGBDataset https://ogb.stanford.edu/docs/graphprop/
- BenchmarkingDataset https://github.com/graphdeeplearning/benchmarking-gnns
Tools
- DGL https://www.dgl.ai/
- Geometric https://pytorch-geometric.readthedocs.io/en/latest/
- OGB https://ogb.stanford.edu/docs/home/
- Benchmarking https://github.com/graphdeeplearning/benchmarking-gnns
Disclaimer
If you have any questions, please feel free to contact us.
Emails: [email protected], [email protected], [email protected]