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 776–800 of 927 papers

Title	Date	Tasks	Status
Rethinking Kernel Methods for Node Representation Learning on Graphs	Oct 6, 2019	ClassificationGeneral Classification	CodeCode Available
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs	Jul 6, 2024	BenchmarkingDataset Generation	CodeCode Available
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification	Jul 22, 2019	ClassificationGeneral Classification	CodeCode Available
Template based Graph Neural Network with Optimal Transport Distances	May 31, 2022	Graph ClassificationGraph Matching	CodeCode Available
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks	Nov 27, 2019	Graph ClassificationRecommendation Systems	CodeCode Available
Understanding Isomorphism Bias in Graph Data Sets	Oct 26, 2019	General ClassificationGraph Classification	CodeCode Available
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification	Jun 2, 2022	Graph Classification	CodeCode Available
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons	Mar 1, 2019	BIG-bench Machine LearningGraph Classification	CodeCode Available
Tensor-Fused Multi-View Graph Contrastive Learning	Oct 20, 2024	Contrastive LearningGraph Classification	CodeCode Available
Kernel Graph Convolutional Neural Networks	Oct 29, 2017	General ClassificationGraph Classification	CodeCode Available
Kernel method for persistence diagrams via kernel embedding and weight factor	Jun 12, 2017	Graph ClassificationTopological Data Analysis	CodeCode Available
Tensor-view Topological Graph Neural Network	Jan 22, 2024	Graph ClassificationGraph Learning	CodeCode Available
k-hop Graph Neural Networks	Jul 13, 2019	General ClassificationGraph Classification	CodeCode Available
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning	Oct 30, 2020	General ClassificationGraph Classification	CodeCode Available
Addressing the Scarcity of Benchmarks for Graph XAI	May 18, 2025	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available
DAGCN: Dual Attention Graph Convolutional Networks	Apr 4, 2019	General ClassificationGraph Classification	CodeCode Available
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning	Sep 22, 2019	DecoderGraph Classification	CodeCode Available
Graph isomorphism UNet	Aug 23, 2023	DecoderGraph Classification	CodeCode Available
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures	Apr 20, 2019	Graph ClassificationTopological Data Analysis	CodeCode Available
The GECo algorithm for Graph Neural Networks Explanation	Nov 18, 2024	Graph Classification	CodeCode Available
An explainability framework for cortical surface-based deep learning	Mar 15, 2022	AnatomyDeep Learning	CodeCode Available
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding	Oct 28, 2023	Graph ClassificationGraph Embedding	CodeCode Available
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends	Mar 1, 2025	ClassificationGraph Classification	CodeCode Available
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space	Dec 7, 2020	Graph ClassificationGraph Learning	CodeCode Available
A Canonicalization Perspective on Invariant and Equivariant Learning	May 28, 2024	Graph ClassificationGraph Embedding	CodeCode Available

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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