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 476–500 of 927 papers

Title	Date	Tasks	Status	Hype
Graph Adversarial Self-Supervised Learning	Dec 1, 2021	Graph ClassificationSelf-Supervised Learning	—Unverified	0
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation	Dec 1, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020	Nov 25, 2021	Graph ClassificationNode Classification	CodeCode Available	1
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification	Nov 22, 2021	ClassificationGraph Classification	CodeCode Available	0
Learnable Structural Semantic Readout for Graph Classification	Nov 22, 2021	ClassificationGraph Classification	—Unverified	0
IV-GNN : Interval Valued Data Handling Using Graph Neural Network	Nov 17, 2021	Graph ClassificationGraph Neural Network	—Unverified	0
Structure Representation Learning by Jointly Learning to Pool and Represent	Nov 16, 2021	ClassificationGraph Classification	—Unverified	0
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks	Nov 11, 2021	Graph ClassificationGraph Regression	CodeCode Available	1
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation	Nov 10, 2021	Data AugmentationGraph Classification	—Unverified	0
Inferential SIR-GN: Scalable Graph Representation Learning	Nov 8, 2021	Graph ClassificationGraph Representation Learning	—Unverified	0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters	Nov 5, 2021	ClassificationGeneralization Bounds	—Unverified	0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices	Nov 5, 2021	ClassificationContrastive Learning	—Unverified	0
Adversarial Attacks on Graph Classification via Bayesian Optimisation	Nov 4, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1
Multi network InfoMax: A pre-training method involving graph convolutional networks	Nov 1, 2021	Graph Classification	—Unverified	0
Topological Relational Learning on Graphs	Oct 29, 2021	Graph ClassificationNode Classification	CodeCode Available	1
InfoGCL: Information-Aware Graph Contrastive Learning	Oct 28, 2021	Contrastive LearningGraph Classification	—Unverified	0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks	Oct 26, 2021	Binary ClassificationGraph Classification	—Unverified	0
Nested Graph Neural Networks	Oct 25, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	1
Watermarking Graph Neural Networks based on Backdoor Attacks	Oct 21, 2021	ClassificationGraph Classification	—Unverified	0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos	Oct 18, 2021	Change Point DetectionGraph Classification	—Unverified	0
Capsule Graph Neural Networks with EM Routing	Oct 18, 2021	Graph Classification	—Unverified	0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs	Oct 18, 2021	ClassificationGraph Classification	—Unverified	0
ifMixup: Interpolating Graph Pair to Regularize Graph Classification	Oct 18, 2021	ClassificationGraph Classification	—Unverified	0
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications	Oct 17, 2021	Graph ClassificationInference Attack	CodeCode Available	1
SoGCN: Second-Order Graph Convolutional Networks	Oct 14, 2021	Graph ClassificationGraph Regression	CodeCode Available	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