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	Hype
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing	Oct 6, 2022	Code ClassificationData Augmentation	CodeCode Available	0
Expander Graph Propagation	Oct 6, 2022	Graph ClassificationGraph Representation Learning	CodeCode Available	1
Geodesic Graph Neural Network for Efficient Graph Representation Learning	Oct 6, 2022	Graph ClassificationGraph Learning	CodeCode Available	1
Graph Classification via Discriminative Edge Feature Learning	Oct 5, 2022	ClassificationGraph Classification	—Unverified	0
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks	Oct 4, 2022	Graph Classification	CodeCode Available	1
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges	Sep 29, 2022	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure	Sep 28, 2022	counterfactualGraph Classification	CodeCode Available	0
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks	Sep 28, 2022	Graph Classification	—Unverified	0
Joint Reconstruction and Parcellation of Cortical Surfaces	Sep 19, 2022	3D ReconstructionGraph Classification	—Unverified	0
Cell Attention Networks	Sep 16, 2022	Graph AttentionGraph Classification	CodeCode Available	0
Graph Contrastive Learning with Cross-view Reconstruction	Sep 16, 2022	Contrastive LearningDisentanglement	—Unverified	0
Explainability in subgraphs-enhanced Graph Neural Networks	Sep 16, 2022	Graph Classification	CodeCode Available	0
SPGP: Structure Prototype Guided Graph Pooling	Sep 16, 2022	Graph ClassificationGraph Learning	—Unverified	0
Graph Perceiver IO: A General Architecture for Graph Structured Data	Sep 14, 2022	Graph ClassificationLink Prediction	CodeCode Available	0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability	Sep 7, 2022	Backdoor AttackGraph Classification	—Unverified	0
Reinforced Continual Learning for Graphs	Sep 4, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	0
Higher-order Clustering and Pooling for Graph Neural Networks	Sep 2, 2022	ClusteringGraph Classification	CodeCode Available	0
A Class-Aware Representation Refinement Framework for Graph Classification	Sep 2, 2022	ClassificationGraph Classification	—Unverified	0
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph Classification	Aug 29, 2022	Graph Classification	CodeCode Available	0
Second-Order Global Attention Networks for Graph Classification and Regression	Aug 27, 2022	ClassificationGraph Classification	CodeCode Available	0
Learnable Filters for Geometric Scattering Modules	Aug 15, 2022	DescriptiveGraph Classification	—Unverified	0
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification	Aug 13, 2022	ClassificationGraph Classification	—Unverified	0
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference	Aug 9, 2022	Graph ClassificationGraph Similarity	—Unverified	0
Generalizing Downsampling from Regular Data to Graphs	Aug 6, 2022	Graph ClassificationRepresentation Learning	CodeCode Available	0
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations	Aug 4, 2022	counterfactualGraph Classification	—Unverified	0

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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