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 451–475 of 927 papers

Title	Date	Tasks	Status	Score
DeepGCNs: Can GCNs Go as Deep as CNNs?	Apr 7, 2019	3D Semantic SegmentationGraph Classification	CodeCode Available	5
Towards Sparse Hierarchical Graph Classifiers	Nov 3, 2018	ClassificationGeneral Classification	CodeCode Available	5
Transformer and Snowball Graph Convolution Learning for Brain functional network Classification	Mar 28, 2023	Graph Classification	CodeCode Available	5
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning	Jun 21, 2020	Graph ClassificationGraph Embedding	CodeCode Available	5
Graph Perceiver IO: A General Architecture for Graph Structured Data	Sep 14, 2022	Graph ClassificationLink Prediction	CodeCode Available	5
DOTIN: Dropping Task-Irrelevant Nodes for GNNs	Apr 28, 2022	Graph ClassificationGraph Learning	CodeCode Available	5
Understanding Attention and Generalization in Graph Neural Networks	May 8, 2019	Graph Classification	CodeCode Available	5
Understanding Isomorphism Bias in Graph Data Sets	Oct 26, 2019	General ClassificationGraph Classification	CodeCode Available	5
Generalizing Downsampling from Regular Data to Graphs	Aug 6, 2022	Graph ClassificationRepresentation Learning	CodeCode Available	5
Capsule Graph Neural Network	May 1, 2019	Graph ClassificationGraph Neural Network	CodeCode Available	5
Graph Representation Learning via Hard and Channel-Wise Attention Networks	Jul 5, 2019	Graph AttentionGraph Classification	CodeCode Available	5
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization	Mar 18, 2020	General ClassificationGraph Classification	CodeCode Available	5
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity	Apr 1, 2019	General ClassificationGraph Classification	CodeCode Available	5
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport	Dec 24, 2019	Graph Classification	CodeCode Available	5
Universal Graph Transformer Self-Attention Networks	Sep 26, 2019	General ClassificationGraph Classification	CodeCode Available	5
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks	Jul 2, 2024	ClusteringGraph Classification	CodeCode Available	5
Verifying message-passing neural networks via topology-based bounds tightening	Feb 21, 2024	Graph Classification	CodeCode Available	5
Graph Self-Supervised Learning with Learnable Structural and Positional Encodings	Feb 22, 2025	Computational EfficiencyGraph Classification	CodeCode Available	5
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees	Jul 9, 2022	Graph ClassificationGraph Embedding	CodeCode Available	5
Wasserstein Weisfeiler-Lehman Graph Kernels	Jun 4, 2019	Graph Classification	CodeCode Available	5
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets	May 4, 2025	Graph Classificationtext-classification	CodeCode Available	5
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	Apr 10, 2017	3D Object Classification3D Point Cloud Classification	CodeCode Available	5
Graph Star Net for Generalized Multi-Task Learning	Jun 21, 2019	ClassificationGeneral Classification	CodeCode Available	5
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available	5
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs	Aug 17, 2024	Graph ClassificationGraph Neural Network	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