Graph Classification

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 501–525 of 927 papers

Title	Date	Tasks	Status
An End-to-End Graph Convolutional Kernel Support Vector Machine	Feb 29, 2020	ClassificationFeature Engineering	—Unverified
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics	Jun 26, 2023	Graph Classification	—Unverified
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks	Sep 7, 2019	Graph Classification	—Unverified
Application of Graph Neural Networks and graph descriptors for graph classification	Nov 7, 2022	ClassificationDrug Discovery	—Unverified
PropEnc: A Property Encoder for Graph Neural Networks	Sep 17, 2024	Computational EfficiencyGraph Classification	—Unverified
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks	Mar 19, 2025	Backdoor AttackGraph Classification	—Unverified
A semantic backdoor attack against Graph Convolutional Networks	Feb 28, 2023	Backdoor AttackClassification	—Unverified
A Structural Feature-Based Approach for Comprehensive Graph Classification	Aug 10, 2024	ClassificationGraph Classification	—Unverified
A Survey on Graph Classification and Link Prediction based on GNN	Jul 3, 2023	Graph ClassificationGraph Neural Network	—Unverified
A Survey on Graph Kernels	Mar 28, 2019	ClassificationGeneral Classification	—Unverified
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources	Nov 30, 2020	ClusteringGraph Classification	—Unverified
Asynchronous Neural Networks for Learning in Graphs	May 24, 2022	Distributed ComputingGraph Classification	—Unverified
Attacking Graph Classification via Bayesian Optimisation	Jun 18, 2021	Adversarial RobustnessBayesian Optimisation	—Unverified
Attacking Graph Convolutional Networks via Rewiring	Jun 10, 2019	General ClassificationGraph Classification	—Unverified
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices	Nov 5, 2021	ClassificationContrastive Learning	—Unverified
Automated Data Augmentations for Graph Classification	Feb 26, 2022	ClassificationData Augmentation	—Unverified
Automated Graph Learning via Population Based Self-Tuning GCN	Jul 9, 2021	Graph ClassificationGraph Learning	—Unverified
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation	Sep 29, 2021	Graph ClassificationNode Classification	—Unverified
Bayesian Deep Learning for Graphs	Feb 24, 2022	Deep LearningGraph Classification	—Unverified
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning	Oct 26, 2019	Bayesian InferenceGeneral Classification	—Unverified
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax	Dec 11, 2019	Bayesian OptimizationGaussian Processes	—Unverified
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning	Nov 22, 2023	BenchmarkingDrug Discovery	—Unverified
Beyond Homophily with Graph Echo State Networks	Oct 27, 2022	ClassificationGraph Classification	—Unverified
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images	Aug 9, 2024	DiagnosticGraph Classification	—Unverified
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search	Feb 6, 2023	Decision MakingGraph Classification	—Unverified

Show:10 25 50

← PrevPage 21 of 38Next →

All datasets PROTEINS MUTAG NCI1 D&D ENZYMES IMDb-B COLLAB PTC IMDb-M NCI109 CIFAR10 100k REDDIT-B

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	GIN-0	Accuracy	762	—	Unverified
2	HGP-SL	Accuracy	84.91	—	Unverified
3	rLap (unsupervised)	Accuracy	84.3	—	Unverified
4	TFGW ADJ (L=2)	Accuracy	82.9	—	Unverified
5	FIT-GNN	Accuracy	82.1	—	Unverified
6	DUGNN	Accuracy	81.7	—	Unverified
7	MEWISPool	Accuracy	80.71	—	Unverified
8	CIN++	Accuracy	80.5	—	Unverified
9	MAGPool	Accuracy	80.36	—	Unverified
10	SAEPool	Accuracy	80.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Evolution of Graph Classifiers	Accuracy	100	—	Unverified
2	MEWISPool	Accuracy	96.66	—	Unverified
3	TFGW ADJ (L=2)	Accuracy	96.4	—	Unverified
4	GIUNet	Accuracy	95.7	—	Unverified
5	G_Inception	Accuracy	95	—	Unverified
6	GIC	Accuracy	94.44	—	Unverified
7	CIN++	Accuracy	94.4	—	Unverified
8	sGIN	Accuracy	94.14	—	Unverified
9	CAN	Accuracy	94.1	—	Unverified
10	Deep WL SGN(0,1,2)	Accuracy	93.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TFGW ADJ (L=2)	Accuracy	88.1	—	Unverified
2	WKPI-kmeans	Accuracy	87.2	—	Unverified
3	FGW wl h=4 sp	Accuracy	86.42	—	Unverified
4	WL-OA Kernel	Accuracy	86.1	—	Unverified
5	WL-OA	Accuracy	86.1	—	Unverified
6	FGW wl h=2 sp	Accuracy	85.82	—	Unverified
7	WWL	Accuracy	85.75	—	Unverified
8	DUGNN	Accuracy	85.5	—	Unverified
9	δ-2-LWL	Accuracy	85.5	—	Unverified
10	CIN++	Accuracy	85.3	—	Unverified