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 226–250 of 927 papers

Title	Date	Tasks	Status	Hype
Recovering Missing Node Features with Local Structure-based Embeddings	Sep 16, 2023	Graph Classification	—Unverified	0
Graph Neural Networks Use Graphs When They Shouldn't	Sep 8, 2023	Graph Classification	CodeCode Available	0
Filtration Surfaces for Dynamic Graph Classification	Sep 7, 2023	ClassificationGraph Classification	CodeCode Available	0
Generalized Simplicial Attention Neural Networks	Sep 5, 2023	Graph ClassificationImputation	CodeCode Available	0
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention	Sep 3, 2023	Graph AttentionGraph Classification	—Unverified	0
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark	Sep 1, 2023	Graph ClassificationGraph Learning	CodeCode Available	1
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images	Aug 31, 2023	Activity PredictionGraph Classification	CodeCode Available	0
Curvature-based Pooling within Graph Neural Networks	Aug 31, 2023	Graph Classification	CodeCode Available	0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts	Aug 31, 2023	Contrastive LearningGraph Classification	—Unverified	0
Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN	Aug 30, 2023	Graph ClassificationGraph Neural Network	—Unverified	0
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems	Aug 28, 2023	Collaborative FilteringGraph Classification	CodeCode Available	1
Universal Graph Continual Learning	Aug 27, 2023	Continual LearningGraph Classification	—Unverified	0
Graph isomorphism UNet	Aug 23, 2023	DecoderGraph Classification	CodeCode Available	0
Cached Operator Reordering: A Unified View for Fast GNN Training	Aug 23, 2023	Graph AttentionGraph Classification	—Unverified	0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors	Aug 22, 2023	Contrastive LearningEEG	—Unverified	0
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding	Aug 22, 2023	Graph ClassificationGraph Regression	CodeCode Available	1
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity	Aug 18, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available	1
Modeling Edge Features with Deep Bayesian Graph Networks	Aug 17, 2023	Graph ClassificationGraph Regression	CodeCode Available	0
S-Mixup: Structural Mixup for Graph Neural Networks	Aug 16, 2023	ClassificationGraph Classification	CodeCode Available	1
Graph Out-of-Distribution Generalization with Controllable Data Augmentation	Aug 16, 2023	Data AugmentationGraph Classification	—Unverified	0
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes	Aug 13, 2023	Graph ClassificationGraph Property Prediction	—Unverified	0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing	Aug 7, 2023	Generalization BoundsGraph Classification	CodeCode Available	0
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification	Aug 4, 2023	Graph ClassificationRetrieval	—Unverified	0
Evaluating Link Prediction Explanations for Graph Neural Networks	Aug 3, 2023	Graph ClassificationLink Prediction	CodeCode Available	0
Counterfactual Explanations for Graph Classification Through the Lenses of Density	Jul 27, 2023	counterfactualCounterfactual Explanation	CodeCode Available	0

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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