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 201–210 of 927 papers

Title	Date	Tasks	Status	Hype
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings	Dec 10, 2023	Graph ClassificationGraph Regression	CodeCode Available	0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning	Dec 9, 2023	Graph ClassificationGraph Learning	—Unverified	0
On the Initialization of Graph Neural Networks	Dec 5, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available	0
Recurrent Distance Filtering for Graph Representation Learning	Dec 3, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available	1
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified	0
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal	Nov 24, 2023	AllClustering	—Unverified	0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning	Nov 22, 2023	BenchmarkingDrug Discovery	—Unverified	0
Hard Label Black Box Node Injection Attack on Graph Neural Networks	Nov 22, 2023	ClassificationGraph Classification	CodeCode Available	0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study	Nov 21, 2023	Data AugmentationFew-Shot Learning	—Unverified	0
Going beyond persistent homology using persistent homology	Nov 10, 2023	Graph 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