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 351–360 of 927 papers

Title	Date	Tasks	Status	Score
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
Graph Classification using Structural Attention	Jul 19, 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
Higher-Order Message Passing for Glycan Representation Learning	Sep 20, 2024	DiversityGraph Classification	CodeCode Available	5
Counterfactual Explanations for Graph Classification Through the Lenses of Density	Jul 27, 2023	counterfactualCounterfactual Explanation	CodeCode Available	5
Bayesian graph convolutional neural networks for semi-supervised classification	Nov 27, 2018	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
Graph Classification with GNNs: Optimisation, Representation and Inductive Bias	Aug 17, 2024	Graph ClassificationInductive Bias	CodeCode Available	5
Geometric deep learning on graphs and manifolds using mixture model CNNs	Nov 25, 2016	Deep LearningDocument 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