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 751–775 of 927 papers

Title	Date	Tasks	Status
Graphon based Clustering and Testing of Networks: Algorithms and Theory	Oct 6, 2021	ClassificationClustering	CodeCode Available
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings	Dec 10, 2023	Graph ClassificationGraph Regression	CodeCode Available
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels	May 30, 2019	Graph Classification	CodeCode Available
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification	Nov 22, 2021	ClassificationGraph Classification	CodeCode Available
Improving the interpretability of GNN predictions through conformal-based graph sparsification	Apr 18, 2024	ClassificationGraph Classification	CodeCode Available
Data-Driven Learning of Geometric Scattering Networks	Oct 6, 2020	DescriptiveGraph Classification	CodeCode Available
Incorporating Heterophily into Graph Neural Networks for Graph Classification	Mar 15, 2022	ClassificationGraph Classification	CodeCode Available
Reinforced Continual Learning for Graphs	Sep 4, 2022	class-incremental learningClass Incremental Learning	CodeCode Available
Supervised Community Detection with Line Graph Neural Networks	May 23, 2017	Community DetectionGraph Classification	CodeCode Available
Relational Pooling for Graph Representations	Mar 6, 2019	General ClassificationGraph Classification	CodeCode Available
Graph Neural Networks with convolutional ARMA filters	Jan 5, 2019	ClassificationGeneral Classification	CodeCode Available
Graph Neural Networks Use Graphs When They Shouldn't	Sep 8, 2023	Graph Classification	CodeCode Available
Verifying message-passing neural networks via topology-based bounds tightening	Feb 21, 2024	Graph Classification	CodeCode Available
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data	Apr 8, 2025	Graph Classification	CodeCode Available
Relation order histograms as a network embedding tool	Jun 9, 2021	GPUGraph Classification	CodeCode Available
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering	Oct 27, 2022	ClusteringData Augmentation	CodeCode Available
Understanding Attention and Generalization in Graph Neural Networks	May 8, 2019	Graph Classification	CodeCode Available
A Novel Higher-order Weisfeiler-Lehman Graph Convolution	Jul 1, 2020	Graph Classification	CodeCode Available
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks	Jun 26, 2023	Graph Classification	CodeCode Available
Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts	Oct 19, 2018	BIG-bench Machine LearningGraph Classification	CodeCode Available
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach	Jan 25, 2025	General KnowledgeGraph Classification	CodeCode Available
Rep the Set: Neural Networks for Learning Set Representations	Apr 3, 2019	General ClassificationGraph Classification	CodeCode Available
Residual Gated Graph ConvNets	Nov 20, 2017	ClusteringGeneral Classification	CodeCode Available
IPC: A Benchmark Data Set for Learning with Graph-Structured Data	May 15, 2019	BenchmarkingGraph Classification	CodeCode Available
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration	Jan 27, 2023	BenchmarkingGraph Classification	CodeCode Available

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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