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 401–425 of 927 papers

Title	Date	Tasks	Status	Score
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing	Aug 7, 2023	Generalization BoundsGraph Classification	CodeCode Available	5
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available	5
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks	May 8, 2024	Graph ClassificationGraph Learning	CodeCode Available	5
Hyperparameter-free and Explainable Whole Graph Embedding	Aug 4, 2021	Graph ClassificationGraph Embedding	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism	Oct 1, 2019	General ClassificationGraph Classification	CodeCode Available	5
Identity Inference on Blockchain using Graph Neural Network	Apr 14, 2021	Graph ClassificationGraph Mining	CodeCode Available	5
Addressing the Scarcity of Benchmarks for Graph XAI	May 18, 2025	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available	5
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs	Oct 21, 2022	Graph Classification	CodeCode Available	5
Attention Models in Graphs: A Survey	Jul 20, 2018	Graph AttentionGraph Classification	CodeCode Available	5
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification	May 11, 2019	ClassificationGeneral Classification	CodeCode Available	5
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets	May 4, 2025	Graph Classificationtext-classification	CodeCode Available	5
How Curvature Enhance the Adaptation Power of Framelet GCNs	Jul 19, 2023	Graph ClassificationGraph Neural Network	CodeCode Available	5
Graph Neural Networks Use Graphs When They Shouldn't	Sep 8, 2023	Graph Classification	CodeCode Available	5
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs	Dec 1, 2017	General ClassificationGraph Classification	CodeCode Available	5
ImageNet Classification with Deep Convolutional Neural Networks	Dec 1, 2012	General ClassificationGPU	CodeCode Available	5
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification	Nov 22, 2021	ClassificationGraph Classification	CodeCode Available	5
On the approximation capability of GNNs in node classification/regression tasks	Jun 16, 2021	ClusteringGraph Classification	CodeCode Available	5
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding	Oct 28, 2023	Graph ClassificationGraph Embedding	CodeCode Available	5
Conditional Prediction ROC Bands for Graph Classification	Oct 20, 2024	ClassificationDrug Discovery	CodeCode Available	5
Graphon based Clustering and Testing of Networks: Algorithms and Theory	Oct 6, 2021	ClassificationClustering	CodeCode Available	5
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks	Nov 27, 2019	Graph ClassificationRecommendation Systems	CodeCode Available	5
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling	Oct 24, 2019	Graph ClassificationRepresentation Learning	CodeCode Available	5
Higher-order Clustering and Pooling for Graph Neural Networks	Sep 2, 2022	ClusteringGraph Classification	CodeCode Available	5
Filtration Surfaces for Dynamic Graph Classification	Sep 7, 2023	ClassificationGraph Classification	CodeCode Available	5

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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