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 26–50 of 927 papers

Title	Date	Tasks	Status	Hype
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing	Mar 1, 2025	Drug DiscoveryGraph Classification	—Unverified	0
Neural Network Graph Similarity Computation Based on Graph Fusion	Feb 25, 2025	Graph ClassificationGraph Similarity	CodeCode Available	0
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning	Feb 24, 2025	Computational EfficiencyGraph Classification	—Unverified	0
Graph Self-Supervised Learning with Learnable Structural and Positional Encodings	Feb 22, 2025	Computational EfficiencyGraph Classification	CodeCode Available	0
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck	Feb 20, 2025	Graph ClassificationGraph Neural Network	CodeCode Available	1
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection	Feb 19, 2025	Claim VerificationDependency Parsing	—Unverified	0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin	Feb 17, 2025	Graph ClassificationGraph Neural Network	—Unverified	0
Classification of Temporal Graphs using Persistent Homology	Feb 14, 2025	ClassificationGraph Classification	CodeCode Available	0
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence	Feb 13, 2025	Graph ClassificationGraph Property Prediction	CodeCode Available	2
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation	Feb 12, 2025	Domain AdaptationGraph Classification	—Unverified	0
Graph Neural Networks at a Fraction	Feb 10, 2025	Graph ClassificationLink Prediction	—Unverified	0
Beyond Message Passing: Neural Graph Pattern Machine	Jan 30, 2025	Graph ClassificationGraph Learning	CodeCode Available	1
Molecular Fingerprints Are Strong Models for Peptide Function Prediction	Jan 29, 2025	Graph ClassificationGraph Regression	CodeCode Available	3
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach	Jan 25, 2025	General KnowledgeGraph Classification	CodeCode Available	0
A Unified Invariant Learning Framework for Graph Classification	Jan 22, 2025	ClassificationGraph Classification	CodeCode Available	0
Catch Causal Signals from Edges for Label Imbalance in Graph Classification	Jan 3, 2025	Graph Classification	CodeCode Available	0
Weakly Supervised Learning on Large Graphs	Jan 2, 2025	ClassificationGraph Attention	—Unverified	0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements	Jan 1, 2025	Contrastive LearningGraph Classification	—Unverified	0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning	Dec 23, 2024	Graph AttentionGraph Classification	—Unverified	0
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing	Dec 23, 2024	Graph Classification	—Unverified	0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning	Dec 23, 2024	Graph ClassificationGraph Representation Learning	CodeCode Available	0
Cluster-guided Contrastive Class-imbalanced Graph Classification	Dec 17, 2024	ClassificationContrastive Learning	—Unverified	0
Semi-Implicit Neural Ordinary Differential Equations	Dec 15, 2024	Graph ClassificationGraph Learning	CodeCode Available	1
Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks	Dec 11, 2024	Graph ClassificationInductive Bias	—Unverified	0
Training MLPs on Graphs without Supervision	Dec 5, 2024	Fraud DetectionGraph Classification	CodeCode Available	1

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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