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 561–570 of 927 papers

Title	Date	Tasks	Status
On Single-environment Extrapolations in Graph Classification and Regression Tasks	Jan 1, 2021	Classificationcounterfactual	—Unverified
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified
On the Expressive Power of Graph Neural Networks	Jan 3, 2024	Graph Classification	—Unverified
On the Expressivity of Persistent Homology in Graph Learning	Feb 20, 2023	Graph ClassificationGraph Learning	—Unverified
On Valid Optimal Assignment Kernels and Applications to Graph Classification	Jun 3, 2016	ClassificationGeneral Classification	—Unverified
Optimisation of Spectral Wavelets for Persistence-based Graph Classification	Jan 10, 2021	ClassificationGeneral Classification	—Unverified
Ordinary differential equations on graph networks	Sep 25, 2019	Graph ClassificationGraph Neural Network	—Unverified
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks	Oct 26, 2021	Binary ClassificationGraph Classification	—Unverified
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis	May 19, 2022	Graph ClassificationLink Prediction	—Unverified
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing	Mar 1, 2025	Drug DiscoveryGraph Classification	—Unverified

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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