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 301–325 of 927 papers

Title	Date	Tasks	Status	Score
An Experimental Study of the Transferability of Spectral Graph Networks	Dec 18, 2020	BenchmarkingGeneral Classification	CodeCode Available	5
Incorporating Heterophily into Graph Neural Networks for Graph Classification	Mar 15, 2022	ClassificationGraph Classification	CodeCode Available	5
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings	Dec 10, 2023	Graph ClassificationGraph Regression	CodeCode Available	5
DEGREE: Decomposition Based Explanation For Graph Neural Networks	May 22, 2023	Graph ClassificationNode Classification	CodeCode Available	5
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data	Apr 8, 2025	Graph Classification	CodeCode Available	5
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks	May 8, 2024	Graph ClassificationGraph Learning	CodeCode Available	5
Learning Deep Graph Representations via Convolutional Neural Networks	Apr 5, 2020	General ClassificationGraph Classification	CodeCode Available	5
An End-to-End Deep Learning Architecture for Graph Classification	Apr 29, 2018	Deep LearningGeneral Classification	CodeCode Available	5
ImageNet Classification with Deep Convolutional Neural Networks	Dec 1, 2012	General ClassificationGPU	CodeCode Available	5
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing	Aug 7, 2023	Generalization BoundsGraph Classification	CodeCode Available	5
Bayesian graph convolutional neural networks for semi-supervised classification	Nov 27, 2018	ClassificationGeneral Classification	CodeCode Available	5
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization	Aug 3, 2024	Contrastive LearningData Augmentation	CodeCode Available	5
Hyperparameter-free and Explainable Whole Graph Embedding	Aug 4, 2021	Graph ClassificationGraph Embedding	CodeCode Available	5
Identity Inference on Blockchain using Graph Neural Network	Apr 14, 2021	Graph ClassificationGraph Mining	CodeCode Available	5
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available	5
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees	May 7, 2022	Graph ClassificationNode Classification	CodeCode Available	5
Decimated Framelet System on Graphs and Fast G-Framelet Transforms	Dec 12, 2020	Graph ClassificationGraph Representation Learning	CodeCode Available	5
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs	Dec 1, 2017	General ClassificationGraph Classification	CodeCode Available	5
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure	Sep 28, 2022	counterfactualGraph Classification	CodeCode Available	5
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels	Apr 21, 2019	Feature EngineeringGraph Attention	CodeCode Available	5
How Curvature Enhance the Adaptation Power of Framelet GCNs	Jul 19, 2023	Graph ClassificationGraph Neural Network	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
Data-Driven Learning of Geometric Scattering Networks	Oct 6, 2020	DescriptiveGraph Classification	CodeCode Available	5
Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts	Oct 19, 2018	BIG-bench Machine LearningGraph Classification	CodeCode Available	5
Analysis and Approximate Inference of Large Random Kronecker Graphs	Jun 14, 2023	Graph ClassificationRecommendation Systems	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