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 501–525 of 927 papers

Title	Date	Tasks	Status
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification	Dec 10, 2022	ClassificationGraph Classification	—Unverified
Graph Contrastive Learning with Implicit Augmentations	Nov 7, 2022	Contrastive LearningGraph Classification	CodeCode Available
Application of Graph Neural Networks and graph descriptors for graph classification	Nov 7, 2022	ClassificationDrug Discovery	—Unverified
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification	Nov 5, 2022	Graph Classification	—Unverified
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations	Nov 4, 2022	Graph Classification	—Unverified
Improving Graph Neural Networks with Learnable Propagation Operators	Oct 31, 2022	Graph Classification	—Unverified
Graph Fuzzy System: Concepts, Models and Algorithms	Oct 30, 2022	Graph ClassificationGraph Clustering	CodeCode Available
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering	Oct 27, 2022	ClusteringData Augmentation	CodeCode Available
Beyond Homophily with Graph Echo State Networks	Oct 27, 2022	ClassificationGraph Classification	—Unverified
HCL: Improving Graph Representation with Hierarchical Contrastive Learning	Oct 21, 2022	Contrastive LearningGraph Classification	—Unverified
Efficient Automatic Machine Learning via Design Graphs	Oct 21, 2022	AutoMLGraph Classification	CodeCode Available
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs	Oct 21, 2022	Graph Classification	CodeCode Available
Test-Time Training for Graph Neural Networks	Oct 17, 2022	Graph ClassificationSelf-Supervised Learning	—Unverified
Boosting Graph Neural Networks via Adaptive Knowledge Distillation	Oct 12, 2022	Graph ClassificationGraph Mining	—Unverified
Pooling Strategies for Simplicial Convolutional Networks	Oct 11, 2022	Graph Classification	CodeCode Available
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing	Oct 6, 2022	Code ClassificationData Augmentation	CodeCode Available
Graph Classification via Discriminative Edge Feature Learning	Oct 5, 2022	ClassificationGraph Classification	—Unverified
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges	Sep 29, 2022	Anomaly DetectionGraph Anomaly Detection	—Unverified
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure	Sep 28, 2022	counterfactualGraph Classification	CodeCode Available
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks	Sep 28, 2022	Graph Classification	—Unverified
Joint Reconstruction and Parcellation of Cortical Surfaces	Sep 19, 2022	3D ReconstructionGraph Classification	—Unverified
Cell Attention Networks	Sep 16, 2022	Graph AttentionGraph Classification	CodeCode Available
Explainability in subgraphs-enhanced Graph Neural Networks	Sep 16, 2022	Graph Classification	CodeCode Available
SPGP: Structure Prototype Guided Graph Pooling	Sep 16, 2022	Graph ClassificationGraph Learning	—Unverified
Graph Contrastive Learning with Cross-view Reconstruction	Sep 16, 2022	Contrastive LearningDisentanglement	—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