Awesome Graph Level Learning

A collection of papers, implementations, datasets, and tools for graph-level learning.

• Awesome Graph-level Learning
  • Surveys
  • Traditional Graph-level Learning
    • Graph Kernels
      • Message Passing Kernels
      • Shortest Path Kernels
      • Random Walk Kernels
      • Optimal Assignment Kernels
      • Subgraph Kernels
    • Subgraph Mining
      • Frequent Subgraph Mining
      • Discriminative Subgraph Mining
    • Graph Embedding
      • Deterministic Graph Embedding
      • Learnable Graph Embedding
  • Graph-Level Deep Neural Networks (GL-DNNs)
    • Recurrent Neural Network-based Graph-level Learning
    • Convolution Neural Network-based Graph-level Learning
    • Capsule Neural Network-based Graph-level Learning
  • Graph-Level Graph Neural Networks (GL-GNNs)
    • Message Passing Neural Networks
    • Subgraph-based GL-GNNs
    • Kernel-based GL-GNNs
    • Contrastive-based GL-GNNs
    • Spectral-based GL-GNNs
  • Graph Pooling
    • Global Graph Pooling
      • Numeric Operation Pooling
      • Attention-based Pooling
      • Convolution Neural Network-based Pooling
      • Global Top-K Pooling
    • Hierarchical Graph Pooling
      • Clustering-based Pooling
      • Hierarchical Top-K Pooling
      • Hierarchical Tree-based Pooling
  • Datasets
  • Tools

---

A Timeline of Graph-level Learning

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]