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 351–375 of 927 papers

Title	Date	Tasks	Status	Score
Learning Convolutional Neural Networks for Graphs	May 17, 2016	Graph Classification	CodeCode Available	5
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification	Jun 28, 2024	ClassificationGraph Classification	CodeCode Available	5
Global Weisfeiler-Lehman Graph Kernels	Mar 7, 2017	General ClassificationGraph Classification	CodeCode Available	5
Cross-Domain Few-Shot Graph Classification	Jan 20, 2022	ClassificationCross-Domain Few-Shot	CodeCode Available	5
Cross-Context Backdoor Attacks against Graph Prompt Learning	May 28, 2024	Backdoor AttackComputational Efficiency	CodeCode Available	5
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs	Dec 1, 2017	General ClassificationGraph Classification	CodeCode Available	5
Bayesian graph convolutional neural networks for semi-supervised classification	Nov 27, 2018	ClassificationGeneral Classification	CodeCode Available	5
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels	Jun 8, 2021	Graph Classification	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
How Curvature Enhance the Adaptation Power of Framelet GCNs	Jul 19, 2023	Graph ClassificationGraph Neural Network	CodeCode Available	5
Counterfactual Explanations for Graph Classification Through the Lenses of Density	Jul 27, 2023	counterfactualCounterfactual Explanation	CodeCode Available	5
Geometric deep learning on graphs and manifolds using mixture model CNNs	Nov 25, 2016	Deep LearningDocument Classification	CodeCode Available	5
AutoGEL: An Automated Graph Neural Network with Explicit Link Information	Dec 2, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	5
Higher-order Clustering and Pooling for Graph Neural Networks	Sep 2, 2022	ClusteringGraph Classification	CodeCode Available	5
Graph Convolutional Networks with EigenPooling	Apr 30, 2019	ClassificationGeneral Classification	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 Message Passing for Glycan Representation Learning	Sep 20, 2024	DiversityGraph Classification	CodeCode Available	5
Cooperative Classification and Rationalization for Graph Generalization	Mar 10, 2024	ClassificationGraph Classification	CodeCode Available	5
Generalized Simplicial Attention Neural Networks	Sep 5, 2023	Graph ClassificationImputation	CodeCode Available	5
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching	Sep 10, 2018	ClusteringCombinatorial Optimization	CodeCode Available	5
Generalization Error of Graph Neural Networks in the Mean-field Regime	Feb 10, 2024	Graph Classification	CodeCode Available	5
A Unified Invariant Learning Framework for Graph Classification	Jan 22, 2025	ClassificationGraph Classification	CodeCode Available	5
Hyperparameter-free and Explainable Whole Graph Embedding	Aug 4, 2021	Graph ClassificationGraph Embedding	CodeCode Available	5
Mirage: Model-Agnostic Graph Distillation for Graph Classification	Oct 14, 2023	BenchmarkingClassification	CodeCode Available	5
GEFL: Extended Filtration Learning for Graph Classification	Jun 4, 2024	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